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Rick Hammell, SPHR, Founder & CEO of Helios, launched the corporate after figuring out a crucial hole in how companies handle world growth. With a background in founding a profitable Employer of File (EOR) providers firm—a mannequin the place a third-party legally employs employees on behalf of a enterprise to simplify world hiring—Hammell acknowledged that many organizations wanted assist past what conventional EOR options supply. Particularly, he noticed that corporations typically struggled with the transition from early-stage worldwide hiring to completely scaling and operationalizing world groups.

Helios was created to satisfy this want by means of a technology-first platform that simplifies how companies handle, interact, and pay their worldwide workforce. The platform presents instruments for automated onboarding, localized compliance, contractor and worker administration, world payroll, and AI-powered insights—all inside a unified system. By decreasing the complexity of worldwide operations, Helios empowers corporations to scale internationally with confidence and compete successfully in at the moment’s borderless financial system.

What motivated you to launch Helios, and the way did your previous expertise in HR and world workforce administration affect the path of the corporate?

The launch of Helios was pushed by a need to streamline world workforce administration, drawing from my intensive expertise in HR. I noticed firsthand the complexities corporations face when managing worldwide groups, and Helios.io was created to simplify these processes with modern know-how.

Helios was constructed with world growth in thoughts. What challenges did you got down to remedy for corporations working throughout borders? 

Helios addresses cross-border challenges by offering a unified platform that manages numerous compliance necessities and cultural variations. It goals to simplify worldwide payroll and HR duties, decreasing administrative burdens and making certain consistency. From the start, we noticed that corporations going by means of world growth had been hindered by fragmented programs and inconsistent compliance requirements. Helios was designed to remove these boundaries by providing a centralized resolution that adapts to native rules, enabling companies to scale confidently and give attention to progress.

AI is usually seen as disruptive, however you’ve got positioned it as supportive. How do you see AI becoming into the fashionable HR tech stack? 

AI in HR is a supportive instrument that enhances effectivity and decision-making. Within the fashionable HR tech stack, AI facilitates data-driven insights, automates routine duties, and empowers HR professionals to give attention to strategic initiatives. We view AI as an enabler that dietary supplements human capabilities somewhat than changing them. By automating repetitive processes, AI permits companies and HR groups to dedicate extra time to strategic planning and worker engagement, fostering a extra dynamic and responsive office.

Are you able to share how your AI assistant, Albert-IQ, helps HR groups make extra knowledgeable choices? 

Albert-IQ assists HR groups by analyzing huge quantities of knowledge to offer actionable insights. It helps in predicting traits, optimizing workforce planning and making certain compliance, enabling extra knowledgeable and strategic decision-making.

In a world the place AI can now deal with duties like onboarding and payroll in seconds, what do you suppose will turn out to be extra necessary for HR groups to give attention to? 

As AI takes over repetitive duties like onboarding and payroll, HR groups might want to focus extra on strategic initiatives, worker engagement, and fostering a optimistic office tradition that drives innovation. The human component stays essential in areas like management growth and cultural alignment. HR professionals and enterprise leaders will play a pivotal function in guiding organizational change and making certain that technological developments translate into significant worker experiences.

In your view, what roles in HR or compliance are ripe for AI augmentation vs. these that may all the time want a human contact? 

Roles involving information evaluation and compliance monitoring are ripe for AI augmentation. Nonetheless, areas like worker relations and culture-building will all the time require the empathy and instinct of a human contact. AI is extremely useful with regards to sorting by means of information and recognizing patterns, particularly in compliance or reporting. Nonetheless, with regards to understanding what motivates folks, constructing belief or guiding somebody by means of a troublesome second, there isn’t any substitute for human connection. These are the moments that outline tradition, and so they require empathy, not algorithms.

Helios gathers an amazing quantity of knowledge. How do you assist corporations translate that into significant insights?

Helios helps corporations through the use of AI to research their information, offering clear, actionable insights that drive strategic choices. This permits companies to make knowledgeable decisions that improve effectivity and progress.

How does Helios differentiate its AI capabilities from different platforms within the world payroll and HCM area?

Helios differentiates itself by offering extremely customizable options that combine seamlessly with current programs, providing real-time insights and predictive analytics tailor-made to particular enterprise wants.

What’s your imaginative and prescient for the way AI will proceed evolving throughout the Helios platform within the subsequent 3–5 years?

Over the following 2–3 years, we envision AI inside Helios evolving to supply much more customized and predictive capabilities, additional enhancing strategic decision-making and enabling proactive HR administration. Our objective is to develop AI functionalities that anticipate organizational wants, equivalent to forecasting expertise gaps and suggesting tailor-made growth applications. By integrating predictive analytics, Helios goals to empower companies to make knowledgeable choices that align with their long-term aims.

Thanks for the nice interview, readers who want to be taught extra ought to go to Helios. 

For hundreds of years, drugs has been formed by new applied sciences. From the stethoscope to MRI machines, innovation has remodeled the way in which we diagnose, deal with, and look after sufferers. But, each leap ahead has been met with questions: Will this know-how actually serve sufferers? Can it’s trusted? And what occurs when effectivity is prioritized over empathy?

Synthetic intelligence (AI) is the newest frontier on this ongoing evolution. It has the potential to enhance diagnostics, optimize workflows, and develop entry to care. However AI is just not resistant to the identical elementary questions which have accompanied each medical development earlier than it.

The priority is just not whether or not AI will change well being—it already is. The query is whether or not it is going to improve affected person care or create new dangers that undermine it. The reply depends upon the implementation decisions we make immediately. As AI turns into extra embedded in well being ecosystems, accountable governance stays crucial. Making certain that AI enhances quite than undermines affected person care requires a cautious steadiness between innovation, regulation, and moral oversight.

Addressing Moral Dilemmas in AI-Pushed Well being Applied sciences 

Governments and regulatory our bodies are more and more recognizing the significance of staying forward of speedy AI developments. Discussions on the Prince Mahidol Award Convention (PMAC) in Bangkok emphasised the need of outcome-based, adaptable laws that may evolve alongside rising AI applied sciences. With out proactive governance, there’s a threat that AI might exacerbate current inequities or introduce new types of bias in healthcare supply. Moral issues round transparency, accountability, and fairness have to be addressed.

A significant problem is the shortage of understandability in lots of AI fashions—usually working as “black bins” that generate suggestions with out clear explanations. If a clinician can’t absolutely grasp how an AI system arrives at a prognosis or therapy plan, ought to it’s trusted? This opacity raises elementary questions on duty: If an AI-driven choice results in hurt, who’s accountable—the doctor, the hospital, or the know-how developer? With out clear governance, deep belief in AI-powered healthcare can’t take root.

One other urgent difficulty is AI bias and knowledge privateness issues. AI programs depend on huge datasets, but when that knowledge is incomplete or unrepresentative, algorithms might reinforce current disparities quite than scale back them. Subsequent to this, in healthcare, the place knowledge displays deeply private info, safeguarding privateness is essential. With out ample oversight, AI might unintentionally deepen inequities as a substitute of making fairer, extra accessible programs.

One promising strategy to addressing the moral dilemmas is regulatory sandboxes, which permit AI applied sciences to be examined in managed environments earlier than full deployment. These frameworks assist refine AI purposes, mitigate dangers, and construct belief amongst stakeholders, guaranteeing that affected person well-being stays the central precedence. Moreover, regulatory sandboxes provide the chance for steady monitoring and real-time changes, permitting regulators and builders to establish potential biases, unintended penalties, or vulnerabilities early within the course of. In essence, it facilitates a dynamic, iterative strategy that allows innovation whereas enhancing accountability.

Preserving the Function of Human Intelligence and Empathy

Past diagnostics and coverings, human presence itself has therapeutic worth. A reassuring phrase, a second of real understanding, or a compassionate contact can ease anxiousness and enhance affected person well-being in methods know-how can’t replicate. Healthcare is greater than a collection of scientific selections—it’s constructed on belief, empathy, and private connection.

Efficient affected person care entails conversations, not simply calculations. If AI programs scale back sufferers to knowledge factors quite than people with distinctive wants, the know-how is failing its most elementary objective. Considerations about AI-driven decision-making are rising, notably relating to insurance coverage protection. In California, almost a quarter of medical health insurance claims had been denied final 12 months, a pattern seen nationwide. A brand new regulation now prohibits insurers from utilizing AI alone to disclaim protection, guaranteeing human judgment is central. This debate intensified with a lawsuit towards UnitedHealthcare, alleging its AI device, nH Predict, wrongly denied claims for aged sufferers, with a 90% error price. These instances underscore the necessity for AI to enrich, not substitute, human experience in scientific decision-making and the significance of sturdy supervision.

The purpose shouldn’t be to exchange clinicians with AI however to empower them. AI can improve effectivity and supply useful insights, however human judgement ensures these instruments serve sufferers quite than dictate care. Medication isn’t black and white—real-world constraints, affected person values, and moral concerns form each choice. AI might inform these selections, however it’s human intelligence and compassion that make healthcare actually patient-centered.

Can Synthetic Intelligence make healthcare human once more? Good query. Whereas AI can deal with administrative duties, analyze complicated knowledge, and supply steady assist, the core of healthcare lies in human interplay—listening, empathizing, and understanding. AI immediately lacks the human qualities obligatory for holistic, patient-centered care and healthcare selections are characterised by nuances. Physicians should weigh medical proof, affected person values, moral concerns, and real-world constraints to make the perfect judgments. What AI can do is relieve them of mundane routine duties, permitting them extra time to deal with what they do greatest.

How Autonomous Ought to AI Be in Well being?

AI and human experience every serve very important roles throughout well being sectors, and the important thing to efficient affected person care lies in balancing their strengths. Whereas AI enhances precision, diagnostics, threat assessments and operational efficiencies, human oversight stays completely important. In any case, the purpose is to not substitute clinicians however to make sure AI serves as a device that upholds moral, clear, and patient-centered healthcare.

Subsequently, AI’s function in scientific decision-making have to be fastidiously outlined and the diploma of autonomy given to AI in well being must be properly evaluated. Ought to AI ever make ultimate therapy selections, or ought to its function be strictly supportive?Defining these boundaries now could be essential to stopping over-reliance on AI that would diminish scientific judgment {and professional} duty sooner or later.

Public notion, too, tends to incline towards such a cautious strategy. A BMC Medical Ethics research discovered that sufferers are extra comfy with AI helping quite than changing healthcare suppliers, notably in scientific duties. Whereas many discover AI acceptable for administrative capabilities and choice assist, issues persist over its influence on doctor-patient relationships. We should additionally think about that belief in AI varies throughout demographics— youthful, educated people, particularly males, are typically extra accepting, whereas older adults and ladies categorical extra skepticism. A standard concern is the lack of the “human contact” in care supply.

Discussions on the AI Motion Summit in Paris strengthened the significance of governance buildings that guarantee AI stays a device for clinicians quite than an alternative to human decision-making. Sustaining belief in healthcare requires deliberate consideration, guaranteeing that AI enhances, quite than undermines, the important human components of medication.

Establishing the Proper Safeguards from the Begin 

To make AI a useful asset in well being, the correct safeguards have to be constructed from the bottom up. On the core of this strategy is explainability. Builders must be required to display how their AI fashions perform—not simply to satisfy regulatory requirements however to make sure that clinicians and sufferers can belief and perceive AI-driven suggestions. Rigorous testing and validation are important to make sure that AI programs are protected, efficient, and equitable. This consists of real-world stress testing to establish potential biases and forestall unintended penalties earlier than widespread adoption.

Know-how designed with out enter from these it impacts is unlikely to serve them properly. With a view to deal with individuals as greater than the sum of their medical information, it should promote compassionate, personalised, and holistic care. To ensure AI displays sensible wants and moral concerns, a variety of voices—together with these of sufferers, healthcare professionals, and ethicists—must be included in its growth. It’s obligatory to coach clinicians to view AI suggestions critically, for the good thing about all events concerned.

Strong guardrails must be put in place to forestall AI from prioritizing effectivity on the expense of care high quality. Moreover,  steady audits are important to make sure that AI programs uphold the very best requirements of care and are in keeping with patient-first rules. By balancing innovation with oversight, AI can strengthen healthcare programs and promote world well being fairness.

Conclusion 

As AI continues to evolve, the healthcare sector should strike a fragile steadiness between technological innovation and human connection. The longer term doesn’t want to decide on between AI and human compassion. As a substitute, the 2 should complement one another, making a healthcare system that’s each environment friendly and deeply patient-centered. By embracing each technological innovation and the core values of empathy and human connection, we are able to be sure that AI serves as a transformative pressure for good in world healthcare.

Nevertheless, the trail ahead requires collaboration throughout sectors—between policymakers, builders, healthcare professionals, and sufferers. Clear regulation, moral deployment, and steady human interventions are key to making sure AI serves as a device that strengthens healthcare programs and promotes world well being fairness.

Proof is mounting {that a} secret lies beneath the dusty crimson plains of Mars, one that would redefine our view of the crimson planet: an unlimited reservoir of liquid water, locked deep within the crust.

Mars is roofed in traces of historic our bodies of water. However the puzzle of precisely the place all of it went when the planet turned chilly and dry has lengthy intrigued scientists.

Our new research might supply a solution. Utilizing seismic knowledge from NASA’s InSight mission, we uncovered proof that the seismic waves decelerate in a layer between 5.4 and eight kilometers under the floor, which might be due to the presence of liquid water at these depths.

The Thriller of the Lacking Water

Mars wasn’t all the time the barren desert we see as we speak. Billions of years in the past, through the Noachian and Hesperian durations (4.1 billion to three billion years in the past), rivers carved valleys and lakes shimmered.

As Mars’ magnetic area pale and its ambiance thinned, most floor water vanished. Some escaped to area, some froze in polar caps, and a few was trapped in minerals, the place it stays as we speak.

4 billion years in the past (high left), Mars might have hosted an enormous ocean. However the floor water has slowly disappeared, leaving solely frozen remnants close to the poles as we speak. Picture Credit score: NASA

However evaporation, freezing, and rocks can’t fairly account for all of the water that will need to have coated Mars within the distant previous. Calculations counsel the “lacking” water is sufficient to cowl the planet in an ocean at the least 700 meters deep, and maybe as much as 900 meters deep.

One speculation has been that the lacking water seeped into the crust. Mars was closely bombarded by meteorites through the Noachian interval, which can have shaped fractures that channelled water underground.

Deep beneath the floor, hotter temperatures would hold the water in a liquid state—not like the frozen layers nearer the floor.

A Seismic Snapshot of Mars’ Crust

In 2018, NASA’s InSight lander touched down on Mars to take heed to the planet’s inside with a super-sensitive seismometer.

By learning a selected sort of vibration referred to as “shear waves,” we discovered a big underground anomaly: a layer between 5.4 and eight kilometers down the place these vibrations transfer extra slowly.

This “low-velocity layer” is more than likely extremely porous rock crammed with liquid water, like a saturated sponge. One thing like Earth’s aquifers, the place groundwater seeps into rock pores.

We calculated the “aquifer layer” on Mars might maintain sufficient water to cowl the planet in a world ocean 520–780 meters deep—a number of instances as a lot water as is held in Antarctica’s ice sheet.

This quantity is suitable with estimates of Mars’ “lacking” water (710–920 meters), after accounting for losses to area, water sure in minerals, and fashionable ice caps.

Meteorites and Marsquakes

We made our discovery thanks to 2 meteorite impacts in 2021 (named S1000a and S1094b) and a marsquake in 2022 (dubbed S1222a). These occasions despatched seismic waves rippling via the crust, like dropping a stone right into a pond and watching the waves unfold.

The crater attributable to meteorite affect S1094b, as seen from NASA’s Mars Reconnaissance Orbiter. Picture Credit score: NASA/JPL-Caltech/College of Arizona

InSight’s seismometer captured these vibrations. We used the high-frequency indicators from the occasions—consider tuning right into a crisp, high-definition radio station—to map the crust’s hidden layers.

We calculated “receiver features,” that are signatures of those waves as they bounce and reverberate between layers within the crust, like echoes mapping a cave. These signatures allow us to pinpoint boundaries the place rock adjustments, revealing the water-soaked layer 5.4 to eight kilometers deep.

Why It Issues

Liquid water is important for all times as we all know it. On Earth, microbes thrive in deep, water-filled rock.

May comparable life, maybe relics of historic Martian ecosystems, persist in these reservoirs? There’s just one approach to discover out.

The water could also be a lifeline for extra complicated organisms, too—akin to future human explorers. Purified, it might present ingesting water, oxygen, or gas for rockets.

After all, drilling kilometers deep on a distant planet is a frightening problem. Nonetheless, our knowledge, collected close to Mars’ equator, additionally hints at the opportunity of different water-rich zones—such because the icy mud reservoir of Utopia Planitia.

What’s Subsequent for Mars Exploration?

Our seismic knowledge covers solely a slice of Mars. New missions with seismometers are wanted to map potential water layers throughout the remainder of the planet.

Future rovers or drills might sooner or later faucet these reservoirs, analyzing their chemistry for traces of life. These water zones additionally require safety from Earthly microbes, as they may harbor native Martian biology.

For now, the water invitations us to maintain listening to Mars’ seismic heartbeat, decoding the secrets and techniques of a world maybe extra like Earth than we thought.

This text is republished from The Dialog beneath a Artistic Commons license. Learn the authentic article.

What really separates us from machines? Free will, creativity and intelligence? However give it some thought. Our brains aren’t singular, monolithic processors. The magic is not in a single “pondering half,” however quite in numerous specialised brokers—neurons—that synchronize completely. Some neurons catalog information, others course of logic or govern emotion, nonetheless extra retrieve reminiscences, orchestrate motion, or interpret visible alerts. Individually, they carry out easy duties, but collectively, they produce the complexity we name human intelligence.

Now, think about replicating this orchestration digitally. Conventional AI was all the time slender: specialised, remoted bots designed to automate mundane duties. However the new frontier is Agentic AI—programs constructed from specialised, autonomous brokers that work together, motive and cooperate, mirroring the interaction inside our brains. Giant language fashions (LLMs) type the linguistic neurons, extracting that means and context. Specialised process brokers execute distinct capabilities like retrieving knowledge, analyzing developments and even predicting outcomes. Emotion-like brokers gauge person sentiment, whereas decision-making brokers synthesize inputs and execute actions.

The result’s digital intelligence and company. However do we want machines to imitate human intelligence and autonomy?

Each area has a choke level—Agentic AI unblocks all of them

Ask the hospital chief who’s attempting to fill a rising roster of vacant roles. The World Well being Group predicts a international shortfall of 10 million healthcare employees by 2030. Medical doctors and nurses pull 16-hour shifts prefer it’s the norm. Claims processors grind by way of limitless coverage critiques, whereas lab technicians wade by way of a forest of paperwork earlier than they’ll even take a look at a single pattern. In a well-orchestrated Agentic AI world, these professionals get some reduction. Declare-processing bots can learn insurance policies, assess protection and even detect anomalies in minutes—duties that will usually take hours of mind-numbing, error-prone work. Lab automation brokers may obtain affected person knowledge straight from digital well being data, run preliminary assessments and auto-generate stories, releasing up technicians for the extra delicate duties that really want human talent.

The identical dynamic performs out throughout industries. Take banking, the place anti-money laundering (AML) and know-your-customer (KYC) processes stay the largest administrative complications. Company KYC calls for limitless verification steps, advanced cross-checks, and reams of paperwork. An agentic system can orchestrate real-time knowledge retrieval, conduct nuanced threat evaluation and streamline compliance in order that workers can concentrate on precise shopper relationships quite than wrestling with types.

Insurance coverage claims, telecom contract critiques, logistics scheduling—the record is limitless. Every area has repetitive duties that lavatory down proficient individuals.

Sure, agentic AI is the flashlight in a darkish basement: shining a brilliant mild on hidden inefficiencies, letting specialised brokers sort out the grunt work in parallel, and giving groups the bandwidth to concentrate on technique, innovation and constructing deeper connections with prospects.

However the true energy agentic AI lies in its capacity to resolve not only for effectivity or one division however to scale seamlessly throughout a number of capabilities—even a number of geographies. That is an enchancment of 100x scale.

• Scalability: Agentic AI is modular at its core, permitting you to start out small—like a single FAQ chatbot—then seamlessly broaden. Want real-time order monitoring or predictive analytics later? Add an agent with out disrupting the remainder. Every agent handles a selected slice of labor, chopping improvement overhead and letting you deploy new capabilities with out ripping aside your present setup.
• Anti-fragility: In a multi-agent system, one glitch gained’t topple every thing. If a diagnostic agent in healthcare goes offline, different brokers—like affected person data or scheduling—maintain working. Failures keep contained inside their respective brokers, guaranteeing steady service. Meaning your whole platform gained’t crash as a result of one piece wants a repair or an improve.
• Adaptability: When rules or client expectations shift, you may modify or substitute particular person brokers—like a compliance bot—with out forcing a system-wide overhaul. This piecemeal strategy is akin to upgrading an app in your telephone quite than reinstalling the complete working system. The consequence? A future-proof framework that evolves alongside your online business, eliminating huge downtimes or dangerous reboots.

You’ll be able to’t predict the following AI craze, however you might be prepared for it

Generative AI was the breakout star a few years in the past; agentic AI is grabbing the highlight now. Tomorrow, one thing else will emerge—as a result of innovation by no means rests. How then, will we future-proof our structure so every wave of latest know-how doesn’t set off an IT apocalypse? In accordance with a current Forrester examine, 70% of leaders who invested over 100 million {dollars} in digital initiatives credit score one technique for fulfillment: a platform strategy.

As a substitute of ripping out and changing outdated infrastructure every time a brand new AI paradigm hits, a platform integrates these rising capabilities as specialised constructing blocks. When agentic AI arrives, you don’t toss your whole stack—you merely plug within the newest agent modules. This strategy means fewer venture overruns, faster deployments, and extra constant outcomes.

Even higher, a strong platform presents end-to-end visibility into every agent’s actions—so you may optimize prices and maintain a tighter grip on compute utilization. Low-code/no-code interfaces additionally decrease the entry barrier for enterprise customers to create and deploy brokers, whereas prebuilt instrument and agent libraries speed up cross-functional workflows, whether or not in HR, advertising and marketing, or another division. Platforms that help PolyAI architectures and a wide range of orchestration frameworks assist you to swap totally different fashions, handle prompts and layer new capabilities with out rewriting every thing from scratch. Being cloud-agnostic, additionally they eradicate vendor lock-in, letting you faucet the most effective AI providers from any supplier. In essence, a platform-based strategy is your key to orchestrating multi-agent reasoning at scale—with out drowning in technical debt or dropping agility.

So, what are the core parts of this platform strategy?

1. Knowledge: Plugged into a typical layer
   Whether or not you’re implementing LLMs or agentic frameworks, your platform’s knowledge layer stays the cornerstone. If it’s unified, every new AI agent can faucet right into a curated data base with out messy retrofitting.
2. Fashions: Swappable brains
   A versatile platform helps you to choose specialised fashions for every use case—monetary threat evaluation, customer support, healthcare diagnoses—then updates or replaces them with out nuking every thing else.
3. Brokers: Modular workflows
   Brokers thrive as unbiased but orchestrated mini-services. In case you want a brand new advertising and marketing agent or a compliance agent, you spin it up alongside present ones, leaving the remainder of the system secure.
4. Governance: Guardrails at scale
   When your governance construction is baked into the platform—protecting bias checks, audit trails, and regulatory compliance—you stay proactive, not reactive, no matter which AI “new child on the block” you undertake subsequent.

A platform strategy is your strategic hedge towards know-how’s ceaseless evolution—guaranteeing that regardless of which AI development takes heart stage, you’re able to combine, iterate, and innovate.

Begin small and orchestrate your approach up

Agentic AI isn’t completely new—Tesla’s self-driving automobiles employs a number of autonomous modules. The distinction is that new orchestration frameworks make such multi-agent intelligence extensively accessible. Now not confined to specialised {hardware} or industries, Agentic AI can now be utilized to every thing from finance to healthcare, fueling renewed mainstream curiosity and momentum.Design for platform-based readiness. Begin with a single agent addressing a concrete ache level and broaden iteratively. Deal with knowledge as a strategic asset, choose your fashions methodically, and bake in clear governance. That approach, every new AI wave integrates seamlessly into your present infrastructure—boosting agility with out fixed overhauls.