Robert Walters research suggests that many Irish organisations are lacking a clear leadership succession plan.

Leadership often defines an organisation and Robert Walters has published data indicating that a number of companies are not as prepared for upcoming changes as they should be. 

The report found that, of those who contributed their data, just 16pc of organisations have a leadership succession plan in place. More than 40pc of Irish companies have no plan in place whatsoever and 7pc are unsure whether one currently exists or not. At the same time, 72pc of Irish leaders said they have a shortage of senior talent, with half describing the shortage as significant.

“There is a clear gap between how concerned organisations are about senior talent shortages and how prepared they are for leadership change,” said Suzanne Feeney, the country manager at Robert Walters Ireland.

She added: “In many organisations, succession planning has historically been handled informally. But they are now operating in a far more complex environment than they were even a few years ago. 

“Advances in artificial intelligence, geopolitical uncertainty and economic pressures are all contributing to more frequent leadership transitions. With only one in five businesses having an established succession plan, many are leaving themselves exposed to significant operational risk.”

Pipeline pressures

Securing and retaining skilled professionals is a key issue for employers in 2026. The recent Data Salaries & Job Sentiment Analysis 2026 report, published by Analytics Institute and SAS, highlighted the growing challenges being experienced by organisations looking to expand their data capabilities. 

The report found that 64pc of organisations have future plans to increase the size of their data teams, whereas 70pc of professionals explained that they are unlikely to change employers this year. 

Commenting on the Robert Walters report, Adam Gordon, the global head of talent development at Robert Walters, said: “Leadership continuity can be a challenge for organisations of every size, from SMEs to the world’s most recognised brands.

“Senior talent is one of the hardest resources to replace and finding the right long-term successor can take time. Interim leaders can play a valuable role here by maintaining stability and ensuring critical decisions continue to move forward while organisations assess their long-term options.”

Robert Walters’ research also points to challenges in the development of future leaders, with the report suggesting that nearly two-fifths (38pc) of participants are struggling to identify and develop strong successors within their business. 

Feeney said: “Many organisations have talented people internally, but identifying future leaders early and giving them the right development opportunities takes deliberate effort.

“At its core, succession planning is about future-proofing the organisation, building a strong leadership pipeline comprising internal progression and external hiring to ensure organisations have the resilience they need for the long term.”

Undoubtedly, the working landscape for modern-day employees is evolving quickly in 2026. An earlier report from Robert Walters, at the start of the year, found that changes in remote and in-person arrangements could compel skilled employees to increase their engagement in the workplace. 

More than half (59pc) of contributing Irish employees said that they want their place of employment to adopt a microshifting schedule, with Feeney noting that microshifting has the potential to increase engagement, accountability and even time spent in the office.

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Over the weekend, hackers stole more than $290 million in cryptocurrency from Kelp DAO, a protocol that allows users to earn yields on idle crypto investments. 

By Monday, LayerZero, one of the projects affected by the hack, accused North Korea of carrying out the heist. The hack is now the largest crypto theft of the year so far, following an earlier hack at crypto exchange Drift in April netted hackers around $285 million.

Per its post on X, LayerZero said the hackers exploited Kelp DAO via its LayerZero bridge, which allows different blockchains to send instructions to each other. The hackers then took advantage of Kelp’s own security configuration, which did not require multiple verifications before approving transactions. That allowed the hackers to siphon off the funds with fraudulent transactions.

The company cited “preliminary indicators” that point to North Korea as the culprit, in particular its hacking group that targets crypto known as TraderTraitor. 

Kelp DAO responded to LayerZero blaming it for the theft instead. 

In the last few years, North Korean hackers working for Kim Jong Un’s regime have become highly successful at stealing crypto. Last year, North Korean hackers stole more than $2 billion in crypto. Overall, since 2017, the total amount of stolen crypto by North Korea is said to be around $6 billion.

The “AI has killed software” narrative has a handful of very loud beneficiaries and a lot of quiet evidence against it. The companies that will survive the next five years are the ones that refuse to treat the hyperscalers as the new gods.

Whenever I make an affirmation, I like to do my research first, and not to sound like a LinkedIn post. I wish more people in this industry did the same, as there is a prevailing mood where we think that big numbers are the whole story.

When the Black Death came among us, people probably thought it was the end. When wars came to our societies, people thought it was the end. Yet, in a strange way, we have a natural power to overcome obstacles and turn change to our advantage.

When AI started to infiltrate our work, and later our personal lives, a large group of people declared that “AI will replace people,” that this technology, not even particularly new, would conquer our brains, hearts, and work, and lead us where it wanted.

Yet we are still working; people are still writing, thinking, creating, building.

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In the last two years, more and more people have been saying that “SaaS is dead.” Of course, this phrase came from someone’s mouth, someone with enough influence to shape general opinion, and everybody was already in black, ready for the funeral.

In August 2024, Klarna’s chief executive, Sebastian Siemiatkowski, sat on an earnings call and mentioned, almost in passing, that the Swedish fintech had “shut down Salesforce.” Workday was next.

Klarna would build its own AI-driven replacements, a lightweight stack unshackled from the bloat of traditional enterprise software. The quote moved markets. Articles followed with headlines about the death of SaaS. Salesforce’s Marc Benioff, on stage at Dreamforce, was asked to respond to a customer who had apparently decided the future was AI and the past was his product. He looked, by his own admission, embarrassed.

Six months later, Siemiatkowski quietly clarified what had actually happened. Klarna had not replaced Salesforce with AI. It had replaced Salesforce with other SaaS: Deel for HR, third-party tools for CRM, the Swedish graph database Neo4j for data consolidation.

Klarna still uses Slack, which is still a Salesforce product. Siemiatkowski himself admitted on X that he was “tremendously embarrassed” by how the story had spiralled.

“No,” he wrote, “we did not replace SaaS with an LLM.”

This is the single most instructive story in enterprise software of the past two years. The distance between what was said and what was done reveals the mechanics of the entire “SaaS is dead” narrative. The headline travelled. The correction did not.

An industry of analysts, venture capitalists, and foundation model CEOs built a year of marketing on the louder half.

Start by asking who gains from the story that software-as-a-service is being replaced by artificial intelligence, because the answer is surprisingly narrow. The hyperscalers do, because AI workloads justify the $660 to $690 billion in capital expenditure the five largest US cloud and technology companies have committed for 2026, according to Futurum Group analysis, nearly double the previous year.

The foundation model labs benefit, because every dollar of enterprise software spend redirected to their APIs validates valuations that are otherwise difficult to defend. OpenAI ended 2025 at around $20 billion in annual recurring revenue. Anthropic crossed $9 billion in January 2026. These are genuinely large numbers. They are also, respectively, about three per cent and a little over one per cent of the hyperscaler capex being spent to serve them.

The venture capitalists benefit because their portfolio repricing depends on the narrative that AI-native companies will outrun the incumbents they once funded. And Nvidia, supplier and financier of the boom, benefits until it no longer does.

In March 2026, CEO Jensen Huang confirmed that his recent investments in OpenAI and Anthropic would likely be the last. The circular financing, Nvidia invests in OpenAI, OpenAI buys Nvidia chips, had reached the point where even the chipmaker was ready to stop calling it a virtuous cycle.

MIT’s Michael Cusumano, quoted by Bloomberg, put the arithmetic bluntly: “Nvidia is investing $100 billion in OpenAI stock, and OpenAI is saying they are going to buy $100 billion or more of Nvidia chips.”

You could call that demand. You could also call it bookkeeping.

The 95% number that should have ended the hype

The harder question is whether any of this is producing business results. Here the data is less generous than the pitch decks.

In July 2025, MIT’s Project NANDA published “The GenAI Divide: State of AI in Business 2025”, based on 150 executive interviews, 350 survey responses, and analysis of 300 public AI deployments. Its headline finding: despite roughly $30 to $40 billion in enterprise generative AI spending, 95% of pilots delivered no measurable impact on profit and loss. Only 5% reached production.

The response from the industry was not to recalibrate. It was to argue that the wrong metric was being used. UC Berkeley published a rebuttal suggesting ROI was an “industrial-era” measurement unsuited to a “cognitive-era transformation.”

This is what every hype cycle says in its late phase, that profit is a distraction, that what is being built is too large for ordinary standards. The same argument was made about WeWork, the metaverse, and blockchain.

Each time, the underlying assumption was that the people with capital and megaphones understood the future better than the people actually trying to run a business.

The 5% of AI projects that did succeed, MIT found, shared specific traits. They were built by specialised vendors, not attempted internally. They focused on back-office automation rather than sales theatre. They integrated deeply with existing workflows. Over half of enterprise AI budgets, meanwhile, were going to sales and marketing tools where ROI was lowest.

This is not a revolution sweeping through the enterprise. It is a lot of companies buying demo-friendly products that do not produce returns, while a minority does the unglamorous integration work that quietly extracts value.

The collapse that did not collapse

Stil, I have to admit that there are genuine signs of stress in the SaaS market. In February 2026, roughly $285 billion in market value evaporated from software stocks in a single trading session, what Wall Street christened the “SaaSpocalypse.”

ServiceNow fell 7%. Intuit dropped 11%. LegalZoom lost nearly 20%. Salesforce is down approximately 30% year-to-date. The business rationale, that per-seat pricing starts to collapse when one employee with AI tools can do the work of five, is not wrong.

But Bain & Company, looking at the broader record, has offered a useful correction: technological transitions rarely produce extinction.

They produce heterogeneity. Desktop survived mobile. Cloud did not kill on-premise so much as push it into specialised niches. The history of software is a history of layers accumulating, not replacing.

SaaS vendors are becoming agent-orchestration platforms. Salesforce has Agentforce. HubSpot has AI tools. Snowflake partners with Anthropic. The incumbents are being forced to adapt, but adaptation is not death.

IDC’s European practice framed it precisely in February: “SaaS is not dead, but it is metamorphosing.”

Pricing shifts towards outcomes. Interfaces become more agent-driven. But the real business logic, the auditing, versioning, compliance, and data gravity, remains where it was. The transformation is real. The extinction event is marketing.

The new gods are not new

Every major technology wave produces a brief period in which the companies at its centre are treated as reinventors of reality. For the cloud, it was AWS. For mobile, Apple. Before that, Microsoft.

The rhetoric around big techs like Nvidia, OpenAI, Anthropic, Meta, and xAI has the same cadence: they are building the new infrastructure of civilisation, rewriting how humans work, inevitable. There is a grain of truth in it. AI, and agentic AI in particular, is a real technological step. 

The companies most likely to thrive are the ones already disciplined enough to recognise the pattern. Every enterprise that survived the dot-com crash, the mobile transition, and the cloud migration did so by adopting what was useful and ignoring what was hyped, by measuring outcomes against costs, by refusing to treat platform vendors as infallible.

The companies that went under bought the whole story: that their customers would wait while they rebuilt, that the new paradigm would reward early and total commitment.

We reported in February on a pattern now visible across dozens of SaaS companies between $20 million and $80 million in ARR: shipping AI features while net revenue retention quietly collapses.

Eighteen months after going “AI-first,” one company watched its NRR drop from 108% to 94% and lost $2.8 million in renewals, not because the product got worse, but because everyone was building the future and nobody was watching the present. The AI features were legitimately good. The existing customers churned anyway.

None of this is an argument against AI. Previous AI cycles ended with research freezes, shuttered startups, and survivors who had been quietly doing useful work while everyone else claimed the moon. This cycle will likely end similarly.

Some hype will turn out to be real. Most revenue projections will not. A handful of current “AI-native” startups will become durable businesses. Many will be absorbed or exposed as wrappers.

The companies that come through refuse both extremes. They do not miss the trend, because dismissing AI in 2026 is as serious a strategic error as dismissing mobile was in 2010. And they do not drown in it. They do not empty their engineering teams into AI-first rebrands while their existing revenue base walks out the door. They do not treat the big tech companies as gods, but as what they are: very large commercial entities with very specific interests in what you believe about the future.

Klarna, for the record, is still paying for SaaS. It is also still paying OpenAI. This is probably the honest shape of the future: not the death of anything, but a quieter rearrangement in which the winners are the operators who kept their feet on the ground while everyone else was watching the sky.

The funeral for SaaS has been extremely well-attended. The corpse, on closer inspection, is still breathing.

Rare earth materials are a hot button topic these days. They’re important for everything from electric vehicles to defence hardware, they’re valuable, and everyone wishes they had some to dig up in their backyard. Lithium, too, is a commodity nobody can get enough of, with the demand for high-performance batteries grows each year.

When a material is desirable, and strategically important, we often start thinking of ways to conserve or recycle it because we just can’t get enough. In that vein, researchers have been developing a new technique to recover rare earth metals and lithium from waste streams so that it can be put back to good use.

Get It Back

Enter the technique of flash joule heating. The method is relatively straightforward, in concept at least. It involves a high energy discharge from a capacitor bank, which is passed through a sample of material to be recycled or refined. The idea is that the rapid energy discharge will vaporize some components of the sample, while leaving others intact, allowing the desired material to be separated out and collected in a straightforward and economically-viable manner.  It does this in a manner rather contrary to traditional techniques, which often involve large amounts of water, acids, or alkalis, which can be expensive and messy to dispose of or reprocess to boot.

Researchers from Rice have developed this technique to recycle rare earth metals from waste magnets. Imagine all the magnets that get thrown away when things like hard drives and EV motors get trashed, and you can imagine there’s a wealth of rare earth material there just waiting to be recovered.

In this case, the high-energy discharge is applied to waste magnet material in an effort to vaporize the non-rare earth components that are present. The discharge is performed in the presence of chlorine gas, which would chlorinate materials like iron and cobalt in the sample, removing the volatile elements and leaving the rare earth elements behind in solid form. Laboratory experiments were able to refine the material to 90% purity in a single step.

As per the research paper, lifecycle analysis suggested the technique could reduce energy use by 87% compared to contemporary hydrometallurgy recycling techniques, while also reducing greenhouse gas emissions in turn and slashing operating costs by 54%.

The technique can also be applied to separate lithium from spodumene ore. It’s an abundant material, particularly in the United States, and improved ways to process it could increase its value as a source of lithium. When it comes to processing spodumene with flash joule heating, the discharge of electric current makes the lithium in spodumene available to react with chlorine gas. The rapid heating causes the vaporized lithium to form lithium chloride which can be bled off, while other components of spodumene like aluminium and silicon compounds remain behind. It’s basically the opposite of the rare earth recovery method.

As outlined in the research paper, this method achieved recovery of lithium chloride with 97% purity and a recovery rate of 94% in a single step. It’s also a lot simpler than traditional extraction methods that involve long periods of evaporating brine or using acid leeching techniques. Indeed, the laboratory rig was built using an arc welder to achieve the powerful discharge. Other researchers are examining the technique too and achieving similar results, hoping that it can be a cleaner and more efficient method of recovery compared to traditional hydrometallurgy and pyrometallurgy techniques.

These methods remain at the research stage for the time being. Pilot plants, let alone commercial operations, are still a future consideration. Regardless, the early work suggests there is economic gain to be had by developing recycling plants that operate in this manner. Assuming the technique works at scale, if it makes financial sense and recovers useful material, expect it to become a viable part of the recycling industry before long.

The New York startup has built AI that reads handwritten fax forms, processes prior authorisations, and completes patient intakes in under five minutes, all without asking providers to change how they work. It has reached multiple millions in revenue in under a year and is targeting 4x growth by end of 2026.

Coral, the New York-based AI startup automating administrative workflows for specialty healthcare providers, has raised $12.5 million in a Series A led by Lightspeed and Z47.

The company was founded in 2024 by Ajay Shrihari, a robotics and AI researcher, and Aniket Mohanty, who has a background in medical image processing.

In under a year of commercial operation, Coral has reached multiple millions in annual revenue and is targeting 4x growth before the end of 2026.

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The problem Coral is solving is not technological complexity, it is administrative volume. In American healthcare, every appointment generates a trail of prior authorisation requests, referral packets, insurance eligibility checks, and discharge paperwork.

Much of this flows through fax machines, which remain deeply embedded in clinical workflows despite being a technology from a previous era.

Rather than attempting to replace fax infrastructure, an approach that would require providers to rebuild systems they cannot afford to rebuild, Coral connects to existing EHR systems, fax lines, and payer portals and automates around them.

Providers do not change how they work. Coral changes what happens inside that workflow.

The company began in the durable medical equipment sector, one of the most fax-intensive corners of outpatient care, where a single order can require multiple rounds of documentation before approval.

DASCO, a home medical equipment provider, has been an early customer, describing turnaround times dropping from hours or days to minutes.

Coral then extended the same model into infusion centres, where a delayed authorisation means a missed dose, not a delayed appointment, and into specialty pharmacy.

In each new vertical, the same administrative bottleneck appeared in the same shape.
The product’s core capability is document understanding at healthcare’s specific level of messiness: handwritten fax forms, scanned insurance cards, prior authorisation templates, and payer portal screens.

Coral’s models have reached 99.7% accuracy across these document types, a threshold the company describes as the minimum viable standard for healthcare, where errors have clinical and financial consequences.

Complete patient intakes, including complex cases, now run in under five minutes. When information is missing, which is frequent in this environment, the platform coordinates with payers, patients, and referral sources to resolve the gap without requiring staff intervention.

The strongest signal in the commercial story is not the revenue figure but the payment behaviour. A portion of Coral’s customers are paying the full contract value upfront, an unusual dynamic in enterprise software, and a striking one in a sector where vendor evaluation cycles are typically slow and risk-averse.

The explanation is mechanical: when a workflow that previously took hours completes in under five minutes at high accuracy, the return on investment is immediate and visible. Commit now, stop the queue now.

Coral recently shipped AI-powered voice and text workflows that automate follow-ups with payers, patients, and referral sources, replacing calls that previously required a staff member to pick up the phone.

The next phase of product development includes an AI workflow builder that will let providers design and deploy their own administrative processes without involving IT, and a co-pilot layer that surfaces operational intelligence from the data already flowing through the platform: which payers have the highest denial rates and why, where cases are stalling in the authorisation process, which referral sources convert reliably and which do not, and what changes would improve outcomes on insurance claim resubmissions.

Rohil Bagga, investor at Lightspeed, described the company as “delivering real outcomes at scale” in an environment where legacy automation has historically failed.

Ashwin KP, investor at Z47, framed the investment thesis around the specific characteristics of healthcare administration: over a trillion dollars in annual overhead, chronically underserved by technology, and requiring deep vertical expertise to crack.

The Series A funds team growth and product development, with Coral adding engineering talent alongside people who have spent careers inside healthcare operations.