Jeonbuk National University Researchers Develop an AI Model For Personalized Blood Glucose Monitoring
The hybrid model integrates three components to address key challenges and was rigorously evaluated, paving way for accurate blood glucose prediction
Patients with Type 1 diabetes (T1D) require accurate and consistent monitoring of their blood glucose levels. Over the past decade, AI models have been explored to tackle this challenge; however, inter-patient variability and large data volumes remain key challenges. In a new study, researchers present BiT-MAML, a model-agnostic algorithm aimed at personalized blood glucose prediction of patients with T1D. This approach overcomes the limitations of existing models and enables precise predictions in real clinical settings.
Type 1 diabetes (T1D) is an autoimmune condition in which the body’s own immune system attacks insulin-producing cells. As a result, patients with T1D must closely monitor their blood glucose (BG) levels and rely on insulin injections or pumps.Even small miscalculations or oversights can lead to unregulated blood sugar levels, leading to potentially life-threatening complications.
Continuous glucose monitoring (CGM) systems have emerged as a promising tool for predicting and forecasting BG levels. Over the past decade, researchers have explored artificial intelligence (AI) models for improving the prediction accuracy of CGM systems. However, differences in physiology between patients and poor adaptation for new users persist to challenge the widespread adoption of this technology in real-world settings. In addition, traditional models often focus on either short-term or long-term glucose patterns, but not both.
In an attempt to address these issues, a research team led by Professor Jaehyuk Cho from the Department of Software Engineering at Jeonbuk National University in South Korea, have developed an innovative model, named BiT-MAML, aimed at tackling inter-patient variability in BG prediction. Explaining further, Prof. Cho says, “BG dynamics are not uniform across all patients. The physiological patterns of an elderly patient are vastly different from those of a young adult.” Adding further, he says, “Our model demonstrates how this variability can be accounted for by developing more personalized models.” Their findings were published in Scientific Reports on August 20, 2025.
BiT-MAML (where “BiT-“ stands for Bidirectional LSTM-Transformer” and “MAML” stands for “Model-Agnostic Meta-Learning”) uses hybrid architecture combining two deep learning models: bidirectional long-short-term memory (Bi-LSTM) and Transformer. Bi-LSTM processes time-series BG data bidirectionally, precisely capturing short-term patterns. Simultaneously, the transformer, utilizing a multi-head attention approach, efficiently models long-term patterns, capturing complex day-to-day and lifestyle-based cyclical variations. During training, the researchers applied a meta-learning approach known as Model-Agnostic Meta-Learning (MAML) that helpsthe model quickly adapt to new and diverse patients using only a small amount of training data by learning from a wide range of patient examples.
To test model performance, the researchers adopted a Leave-One-Patient-Out Cross-Validation (LOPO-CV) scheme. “In simple terms, we train the AI on five patients, then test it on the sixth patient it has never seen before,” explains Prof. Cho. “This is effective for assessing the model’s ability to generalize to unseen patients.”
The model demonstrated significantly reduced prediction error compared to conventional models. Notably, the prediction error varied from an excellent 19.64 milligram/decilitre (mg/dL) for one patient to a challenging 30.57 mg/dL for another. While these results represent a clear improvement over the standard LSTM models, they also highlight the persistent difficulty of managing inter-patient variability in real-world settings. “Our study shows how AI-based BG prediction models should be evaluated to improve both trust and model performance,” concludes Prof. Cho. “Addressing this challenge will contribute to the development of effective CGM models that can serve diverse patients with T1D, from children to elderly.”
These findings attest to the fact that the development of effective personalized BG prediction requires the use of advanced AI models incorporating robust evaluation methods that can transparently report the full spectrum of performance.
Reference
Title of original paper: Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model
In the pursuit of digital transformation, businesses often spotlight their cutting-edge applications, their multicloud strategies, or their latest AI models. Yet, behind each of these advancements lies a powerful, unseen engine: the enterprise storage platform. Ivor Buckley, Field CTO, Dell Technologies Ireland tells us more below
Once regarded as a back‑end system, enterprise storage has become a strategic platform that underpins innovation. As Irish organisations race to modernise services, comply with regulation and compete internationally, the way they store, protect, and govern data is turning into a fundamental differentiator.
Today’s IT leaders face a significant challenge. They must support an ever-expanding portfolio of workloads, from critical business databases to cloud-native applications and data-intensive AI projects. All this must be achieved within the constraints of tight budgets and limited staffing. The sheer volume of data being created and managed is staggering; global data generation is expected to reach 393.9 ZB by 2028 as per IDC. This explosion of information puts immense pressure on infrastructure that was not designed for this scale or complexity resulting in data foundations under strain
According to the latest Dell Innovation Catalyst Study, 48% of Irish organisations are prioritising data readiness for AI related workload, while 66% say they are still in their early or mid-stage of their AI/GenAI journey. This underscores a reality that organisations want to innovate but their data foundations and current storage systems are not fully equipped.
From Data Silo to Intelligent Hub
The perception of enterprise storage as a mere commodity is outdated. Modern platforms have become intelligent hubs that automate complex tasks and unlock new efficiencies. By integrating machine learning and advanced analytics, today’s storage systems can proactively optimise workload placement, predict performance bottlenecks before they occur, and simplify management tasks that once consumed countless hours.
This shift is relevant in Ireland, where businesses from multinationals to SMEs are accelerating digital transformation under the National AI Strategy. A study Dell undertook found that 96% of Irish organisations face challenges when it comes to identifying, preparing, and using data for AI/GenAI uses cases, with 40% struggle to integrate AI systems with existing IT infrastructure. Intelligent storage platforms directly address these pain points by reducing complexity and improving data accessibility without creating new data silos
For Irish businesses planning to expand their e-commerce operations and presence, a modern storage platform can intelligently prioritise these diverse workloads, ensuring that customer-facing applications remain responsive while they have high-speed access, they need to train their models that maintain the strategic initiatives that drive business growth.
Bridging Private Cloud and Multicloud for Seamless Innovation
In today’s digital landscape, businesses are increasingly faced with the decision to operate within a private cloud, adopt a multicloud environment, or find a balance between the two. Enterprise storage serves as the reliable backbone for these evolving strategies, delivering the infrastructure needed to provide both security and agility at scale.
For Irish businesses relying on private cloud infrastructure, enterprise storage provides robust data protection, predictable performance, and the confidence that sensitive information remains under their control. As organisations here in Ireland expand further into multicloud setup, seamless data mobility becomes essential not just for storing data but also for making it accessible and secure wherever it resides.
According to the Dell study, 46% of local organisations plan to modernise their IT with intelligent infrastructure, and another 46% aim to optimise workload placement across edge, core, and cloud environments.
The right storage platform is central to both goals: it can synchronise data across environments, break down silos and help ensure that everyday operations remain stable even as new services and AI projects come online.
This reflects a clear shift towards hybrid architecture, a trend mirrored in Ireland’s public-sector digital transformation and the country’s growing cloud smart enterprise landscape.
Crucially, enterprise storage also addresses security, and compliance demands unique to both private and multicloud models. By providing unified management and strong governance features, these platforms make it easier for businesses across Ireland to implement consistent security policies and adhere to regulatory requirements. The result is an IT environment that’s not only flexible and responsive but also protected, adhering to regulation and aligned with business goals.
Fuelling the Future of AI and Analytics
Perhaps the most significant driver of storage innovation today is AI. AI and machine learning workloads are incredibly data-hungry, requiring massive datasets to be fed to powerful processors without delay. A bottleneck in the storage layer can bring an entire AI initiative to a standstill.
Modern enterprise storage platforms are engineered to meet these demands, delivering the high throughput and low latency needed to fuel advanced analytics. A healthcare provider, for instance, might use AI to analyse medical images to detect diseases earlier. This process requires rapid access to petabytes of high-resolution image data. An intelligent storage system ensures that this data is readily available, accelerating the model training process and ultimately improving patient outcomes.
One of the most significant developments in this space is the emergence of the data lakehouse – a modern data architecture that blends the flexibility of a data lake with the performance and governance of a data warehouse.
Rather than forcing organisations to move and duplicate data repeatedly into different silos, a Data Lakehouse strategy is about bringing AI to the data. By minimising unnecessary data movement and providing a single point of access, it helps address some of the biggest blockers to AI projects: fragmented data, inconsistent governance, and slow time‑to‑insight.
Modern Enterprise Storage Has Become the Unseen Engine of Digital Innovation
The journey of enterprise storage reflects the broader story of technological progress. What was once a simple utility has become a strategic enabler for Cloud, AI and data-driven services, quietly powering the applications and insights that define modern business. By embracing automation, enabling seamless data mobility, and delivering the performance needed for next-generation workloads, enterprise storage has become the unseen engine of digital innovation.
Irish businesses are operating in one of Europe’s most dynamic digital economies and the opportunity is clear. Ireland’s National AI Strategy aims to see 75% of Irish enterprises using cloud, AI, and data analytics by 2030. To fully realise this potential, businesses must proactively evaluate, adopt, and integrate these advanced solutions into their Cloud Operating Model. This isn’t just about keeping up, it’s about unlocking new levels of efficiency, innovation, and competitiveness. By investing in vital storage infrastructure, businesses of all sizes can simplify data management, scale with confidence, and accelerate their AI journey for the next wave of AI-driven transformation.
Computer vision is powering everything across Ireland’s fast-growing tech ecosystem, from advanced manufacturing and smart retail to fintech security. Data annotation sits at the core of these intelligence systems. Keep reading to understand how Irish tech companies are improving accuracy and accelerating model training as AI-powered annotation systems become scalable and precise.
Data Annotation Trends in Irish Tech Companies
Many Irish tech companies in the early computer vision development relied on small teams, mostly in-house, to label videos and images manually. These processes were inconsistent, slow and expensive, especially during scaling or when datasets reach the millions. Now, companies are relying on AI-powered data annotation to reshape their workflow. By combining human validation with automated pre-labelling, providers like theoWorkers team offer support in handling large-scale datasets with great precision and speed. This is a hybrid approach that allows both established businesses and startups to train their vision models with great efficiency without compromising quality.
Data annotation plays an essential role in system training, since even the most sophisticated AI model is as accurate as the data it trains from. Irish companies are taking advantage of well-annotated datasets for different sectors like retail analytics, fintech, health tech and smart cities to power fraud prevention, facial recognition, predictive maintenance and object detection. AI-powered tools are gaining popularity since they reduce human errors, speed up turnaround and guarantee consistent labelling standards across different projects. Because of that, organisations can scale their computer vision solutions confidently, improve model performance and shorten development cycles in competitive global markets.
How AI-Powered Annotation Elevates Models Accuracy
Companies cannot achieve accurate computer systems by chance; they should build them on precisely labelled data. Improving model accuracy and developing AI-driven platforms for Irish tech organisations is directly tied to the consistency and quality of annotation processes.
Machine Learning Pre-Labelling
Machine learning models are used by AI-powered annotation tools to automatically create initial labels for videos and image frames. This pre-labelling technique helps companies reduce workloads and accelerate dataset preparation. The only work annotators have is to review and refine already generated tags, segmentation masks and/or bounding boxes instead of starting from scratch. For Irish companies working under pressure, this means quicker deployment and faster iterations of computer vision solutions.
Human Validation (In the Loop)
Human experience and expertise remain vital even though automation alone speeds up workflows. Human-in-the-loop validation guarantees that any AI-generated annotation is checked for edge cases, context and nuance. Skilled reviewers in this approach handle complex scenarios, correct inaccuracies and maintain dataset consistency. This is a perfect combination of precision and speed, which results in a stronger model performance and reliable training data.
Bias Reduction and Feedback Loops
AI-assisted annotation systems “grow” over time through a well-structured feedback loop. This means that corrections made by human annotators are returned to the systems to refine future output. Because of that, companies can boost efficiency while identifying and minimising bias in datasets. Reducing bias, especially for Irish tech companies like healthcare, finance and smart cities, is vital for fairness, long-term trust and compliance.
Conclusion
AI-enhanced data annotation is taking centre stage in computer vision innovation in Ireland‘s tech companies. These organisations can develop reliable, scalable and more accurate AI systems by combining human expertise with intelligent automation.
Basketball analytics has experienced a revolution that is just as big as the industrial revolution. What we have moved away from is a cottage industry of manual tabulation and have gone to a high-fidelity, automated surveillance state. To data scientists and hardcore bettors who have to design predictive models for NBA Player props, this transition is a complete change in the unit of analysis. We have left the discrete and retrospective, the simple box score, behind and entered the continuous and probabilistic world of the spatiotemporal tracking.
Bookmaker algorithms are very efficient in the new betting ecosystem. Using “Macro-Level” statistics, such as the Points Per Game (PPG), is a clear drawback in competition. The exploitable edge, the Alpha, has been moved to the Micro-Level data, the X, Y, Z position of the players recorded at 25 frames per second. This paper outlines the theoretical models and operational procedures necessary to create state-of-the-art feature engineering pipelines to predict individual players’ performance beyond the box score by modeling the process, not just the result.
The Data Ecosystem: Building the Foundation
A predictive engine is based on the infrastructure of its data. To the NBA Player Prop modelers, the ecosystem is hierarchical, whereby disparate data sources must be combined based on their latency and granularity differences. The knowledge of this order is the initial step to creating a model that will be able to outperform the market.
The Hierarchy of Data Granularity
The modern data pipeline processes three distinct strata of information, each offering unique insights and requiring specific engineering approaches:
Box Score Data (Structured/Low-Latency): This forms the foundation of historical analysis. It tells us what happened—LeBron James scored 25 points—but not how. Although it would work well with ground truth targets, its predictive capability is restricted by the fact that it is retrospective.
Play-by-Play Data (Sequential/Event-Based): This layer provides a chronological sequence of events. It is essential in converting the so-called contextual features, including lineup-specific usage rates. With substitution logs, it is possible to compute the performance splits of a player when particular teammates are on or off the floor, which is an essential part of nullifying projections when receiving breaking injury news.
Tracking Data (Spatio-Temporal/High-Volume): This forms the frontier of analytics. This data is originally offered by SportVU and currently by Second Spectrum and is a set of coordinates of every player and the ball. It enables one to calculate velocities, accelerations, and inter-player distances.
The Alignment Problem
One of the ongoing engineering challenges is the “Alignment Problem. There are usually inconsistencies between manually recorded timestamps in Play-by-Play (PBP) logs and tracking data generated by the machine. To generate reliable training sets (such as training a model to predict whether or not a shot will be successful based on the distance of the defenders), these streams need to be synchronized via the use of fuzzy matching algorithms or by detecting the abrupt change in the ball velocity to identify the frame of a shot.
Temporal Dynamics: Modeling Time, Fatigue, and Schedule
In NBA Player Props, the basic assumption of the performance of a player being independent and identically distributed (i.i.d.) is incorrect. Performance is a time-series phenomenon that is heavily affected by biological limitations of the human body and logistical strictness of the NBA schedule.
The Mathematics of “Recent Form”
The reason why static season averages are not good predictors is that they fall behind in the position or physical shape of a given player. Recency should be given priority in feature engineering, coupled with stability in the sample size.
Exponentially Weighted Moving Averages (EWMA): EWMA does not use an ordinary moving average but rather uses exponentially decreasing weights for the aged observations. This is better at identifying the breakout players whose position has permanently changed because of a change in the lineup or coaching decision.
Rolling Window Variance: In addition to the mean, the variance of a player is a very important feature. A player whose variance in shooting splits is large is a more dangerous bet to place on an over bet, but can be of huge value in an alternate line market where tail outcomes tend to be inefficiently priced.
Circadian Biology and Schedule Fatigue
The NBA schedule is a complicated variable, which creates physiological strain. It is necessary to encode this stress in smart models in order to predict diminished performance.
Rest Matrices: There is a statistically significant negative Effective Field Goal Percentage (eFG%) and Defensive Rating on 0 days rest (Back-to-Backs), and it has been observed to be especially true among high-usage veterans.
The “3-in-4” and “5-in-7”: Binary flags on schedule density (3 games per 4 nights) are used to define schedule losses, where player output is minimized in all parts of the board.
Altitude Adjustment: Aerobic capacity is affected by games that are played in elevated areas such as Denver or Salt Lake City. This attribute has to be heavily incorporated in predictive models of 4th-quarter props because starters tend to have fewer minutes or reduced efficiency in the later parts of games.
In order to forecast the amount of production (Points, Rebounds, Assists) in NBA Player Props, it is necessary to know the quality of the role and efficiency of the player. There is no more data, the artifacts of these underlying drivers, which are raw box score counts.
True Shooting and Shot Selection
Field Goal Percentage (FG%) is a very primitive statistic that considers all shots equal. Current-day modeling is based on derivatives such as the True Shooting Percentage (TS%), which uses both free throws and 3-pointers. TS percent is very predictive since it reflects the capability of a player to produce points in the line, which is a skill that is not as fluctuating as jump shooting. It is common to identify players with large TS% and small recent point totals as a good opportunity to buy, since their efficiency predicts that point totals will be recovered positively when volume returns to normal.
Usage Dynamics and The “Wally Pipp” Effect
Usage Rate (USG%) approximates the level of team plays utilized by a player on the floor. But there is not enough historical usage when the injuries strike. The concept of the redistribution of opportunity, as a result of an injury to a starter, due to which the opportunity is lost, is called the Wally Pipp effect. Dynamic Usage Projections should be a part of feature engineering. In case of a high-usage star being sidelined, his/her holdings are forced to be taken up by other players who are left on the roster. With/Without query features are used by the models to forecast the new hierarchy, and lineup-level data is processed to compute usage differentials, player-specific.
The Physics of Basketball: Optical Tracking Features
Quantified Shot Quality (qSQ) is, perhaps, the most powerful predictor of regression. This measure utilizes the XY-intercepts of the shooter and all the defenders to determine the likelihood of a shot being made, regardless of the eventual outcome.
Quantified Shot Quality (qSQ) and Expected Points
Luck can be detected by determining the Shot Quality Delta (Actual eFG% – Expected eFG%). A very positive delta is an indication of a player who is running hot (taking unsustainable shots), which indicates a Sell or Under bet. A negative delta is a bad omen on good shots, representing a “Buy” or an Over bet.
The Geometry of Rebounding
Rebounding has been considered as an effect of effort, but tracing data indicates that it is, in most cases, an effect of geometry.
Voronoi Tessellation: The court is divided into areas depending on the location of players. The most common theoretical probability of the rebound will be the player who currently has the biggest Voronoi region around the rim when he or she misses the ball.
Deferred Rebound Rate: This is a measure of how the percentage of uncontested rebound opportunities a player passes to a teammate.
Adjusted Rebound Rate: This measure isolates the Contested Rebound Rate. Proficiency in this area means that the players will be able to resist difficult playing situations compared to stat-padders, who are dependent on board space.
Potential Assists and the “Passer’s Bias”
Assists are obnoxious since they are based on the receiver’s shooting. The process of playmaking is measured by Potential Assists, which are a pass that results in a shot attempt. When a player has a high potential assists and low actual assists, then his or her conversion rate is probably experiencing variance. Their future help would be projected by a predictive model and regressed to the mean, with this detecting that the box score is missing.
Quantifying Defense: The Holy Grail of Context
The most important contextual variable in prop prediction is modeling the defense of the opponent. Nonetheless, such standard measures as Opponent Points Allowed are not enough. We have to design functions that pick out a certain matchup dynamics.
Hidden Markov Models for Matchup Estimation
We cannot just assume positions guard positions (e.g., PG guards PG). Currently, defenses change and cross-match. Hidden Markov Models (HMM) are the models used to predict the player who will be guarding the target player. The hidden variable is the defensive state, and the observable emissions are the spatial locations of the players. This then enables us to build a weighted Matchup Difficulty Score, which is player-specific.
Scheme Identification
Defenses employ different tactical schemes (Drop, Hedge, Blitz, Switch).
Aggression+: A metric of the frequency with which a defense uses two defenders on the ball.
Variance+: Quantifies the frequency of a change in coverage of the defense. Terms of interaction are important here. A ball handler, with high turnover, against a high “Aggression+” defense is a good indication of “Over Turnovers” props. On the other hand, a pull-up shooter compared to a drop coverage scheme is considered more efficient by projection.
Machine Learning Architectures and Feature Selection
These features are complicated and demand advanced techniques of modeling, as they will prevent over-fitting and non-linear interactions.
Dimensionality Reduction: As tracking data produces millions of data points, compressing data on trajectories into understandable ways that can be interpreted requires methods such as Principal Component Analysis (PCA) and Non-Negative Matrix Factorization (NMF).
Gradient Boosting (XGBoost/LightGBM): They are the industry standards of tabular sports data, and do well with the non-linearities, and offer metrics of feature importance.
Graph Neural Networks (GNNs): An innovative strategy that constitutes the court as a graph, with the players being the nodes and the interactions being the edges. GNNs can uniquely be learned on tracking data, learning complicated dynamics of chemistry and spacing.
The Betting Market: Execution and Strategy
The predictive model can only be useful to the extent to which it has been applied to the market. The last step will be locating inefficiencies and controlling your bankroll.
Market Inefficiencies
The “Under” Bias: There is a psychological bias among people towards Overs (rooting against action). As a result of this, lines are usually overstated by bookmakers. Models will tend to have a higher Expected Value +EV on “Under” bets, especially when it comes to role players whose mileage is shaky.
Rotation Risk: The minutes distribution is not normal. Depending on the score of the game (blowout risk), starters may play 35 minutes or 28 minutes. It is important to model the distribution of the minutes and not just the mean.
The Kelly Criterion
Bet sizing must be Kelly-based (betting by the Kelly Criterion) to maximize long-term growth, which is computed by the Kelly Criterion based on your edge and odds. Since NBA Player Props are highly varying, practitioners frequently apply the strategy of the fractional Kelly (e.g., bet half of the recommended value) in order to eliminate the effect of a volatile bankroll and, nevertheless, gain the benefit of the model.
In an era when nearly every business service has migrated online—from banking to consultations, from meetings to training courses—one Irish company has built over a decade of success doing the exact opposite. Their counterintuitive approach offers valuable lessons about when digital-first strategies actually work against business goals.
Since 2013, SafeHands Health & Safety Solutions has maintained a strictly on-site training model, delivering workplace safety training at client premises across Ireland. They’ve built partnerships lasting over 10 years, earned a 4.7/5 rating on Trustpilot, and demonstrated that some services genuinely work better when delivered in person.
Their success raises an important question for Irish business owners: Are we digitising services because it genuinely improves outcomes, or simply because “digital-first” has become the default assumption?
The Digital Training Boom and Its Limitations
The pandemic accelerated online training adoption dramatically. Businesses discovered they could deliver compliance training through video platforms, record sessions for later viewing, and eliminate travel time entirely. The operational efficiencies seemed obvious.
Yet completion rates told a different story. Online training courses often see completion rates below 30%. Participants log in, leave videos running in the background whilst working on other tasks, and retain minimal information. The certificate gets issued, compliance boxes get ticked, but actual knowledge transfer remains questionable.
More importantly, certain types of training require hands-on practice with actual equipment, in real environments, addressing specific workplace challenges. You can watch videos about proper lifting techniques, but without practicing on your actual equipment, in your actual workspace, with your actual workflows, the knowledge rarely translates into changed behaviour.
The On-Site Advantage: Learning in Context
SafeHands delivers all training on-site at client premises across Ireland, from Dublin offices to coastal hotels in County Clare. This operational choice creates immediate practical advantages that digital alternatives cannot replicate.
David McManus from Bellbridge House Hotel in Spanish Point, Clare, experienced this approach firsthand: “It was so professional from the booking to the day of the training. Nothing was an issue. We had to change dates due to weather, no issue. The staff found the training interesting and very informative.”
When training happens in the actual workplace, several things occur that digital training cannot achieve:
Immediate Context: Staff learn using their real equipment, not generic examples. A restaurant team learning food safety and HACCP procedures works with their actual kitchen layout, their specific equipment, and their real menu items.
Practical Application: Hands-on practice with the tools and equipment staff use daily ensures skills transfer immediately. Watching a video about fire extinguisher use differs enormously from actually handling the extinguisher mounted in your corridor.
Customised Content: Instructors observe actual workplace conditions and can address specific challenges that generic online courses never anticipate. Every workplace has unique characteristics that affect how safety principles apply.
Team Learning: When entire teams train together in their workspace, they develop shared understanding and can discuss how procedures apply to their specific operations.
Nisheeth Tak from Rasam Restaurant in Dublin shares their experience: “We have been using SafeHands for all our health and safety programmes for years. We have benefitted enormously from their professional guidance and up-to-date knowledge of the legislation.”
That phrase “for years” appears repeatedly in client testimonials—a pattern suggesting genuine value rather than grudging compliance spending.
The Business Model: Long-Term Relationships Over Transactions
Perhaps the most interesting aspect of SafeHands’ approach involves how on-site delivery enables different client relationships than digital training platforms create.
The Irish Association for Counselling and Psychotherapy has worked with SafeHands for over 10 years. ALSAA Bowl has maintained their partnership since 2015. These aren’t isolated examples—sustained multi-year relationships appear consistently across their client base.
Carol Murray from IACP explains their decade-long partnership: “The IACP has been using Safe Hands now for over 10 years. They look after all of the Fire Safety Training and Fire Warden Training for our staff. I have found them to be very accommodating and reliable.”
Ten years with a single training provider is remarkable in an industry where businesses typically shop around for the cheapest compliant option. This pattern suggests several things about their business model:
Consistent Quality: Organisations don’t maintain decade-long partnerships with providers who deliver inconsistent service. Reliability at scale requires operational discipline that many businesses never achieve.
Institutional Knowledge: When providers work with the same clients over years, they develop understanding of specific operational contexts that improves service quality over time. Initial consultations become unnecessary. Training builds on previous sessions rather than starting from scratch.
True Partnership: The language in testimonials—”accommodating,” “reliable,” “pleasure to deal with”—signals relationships that transcend transactional service delivery. Digital platforms rarely generate this kind of client loyalty.
Alison Kealy from Kealy’s of Cloughran in Dublin captures this: “We use SafeHands for all our Staff Training and Health and Safety Consultancy. Noel is a pleasure to deal with, and they always provide the services we need.”
The Operational Challenge: Scaling Personal Service
On-site service delivery creates operational complexity that digital platforms avoid entirely. Coordinating instructor schedules across Ireland, managing travel logistics, accommodating client timing needs, and maintaining consistent service quality despite geographic dispersion all require sophisticated operational capability.
Yet this complexity creates competitive moats that purely digital competitors cannot easily cross. When a business master complex operations, replication becomes difficult. Generic online training platforms can launch quickly. Building operational excellence across physical service delivery takes years.
JR Labels experienced this operational reliability: “This is our second time using SafeHands. Everyone we dealt with couldn’t have been more helpful. Our Manual Handling training was delivered in a professional manner and we will happily use SafeHands again in the future.”
The phrase “second time” indicates clients who measured value and deliberately chose to reinvest—the ultimate business validation.
Payment Models: Digital Systems Supporting Physical Service
Interestingly, SafeHands does leverage digital systems where they create genuine value. Payment infrastructure uses Stripe alongside traditional bank transfers and telephone payments, with all fees payable upfront.
This payment approach demonstrates strategic technology adoption. Digital payment systems remove friction, improve cash flow, and reduce administrative burden. But the service itself—the actual training delivery—remains resolutely physical because that’s where value gets created.
This selective digitisation offers a model for other Irish businesses: use digital tools where they solve real problems, but don’t digitise services simply because “digital-first” sounds modern.
When Digital Works and When It Doesn’t
SafeHands offers one online option—mental health awareness training—recognising that some content genuinely works in digital formats. Theoretical knowledge, awareness building, and conceptual understanding can transfer effectively through online platforms.
But manual handling training, fire safety practice, food preparation procedures, and emergency response drills require hands-on experience that video cannot replicate. Your body needs to practice correct lifting techniques. Your hands need to feel how fire extinguishers operate. Your team needs to rehearse emergency procedures in your actual workspace.
Laura Devlin, HR Manager at Cabra Castle Hotel in Cavan, emphasises the value of this physical delivery: “We used SafeHands again for our Food Safety/HACCP training for our kitchen staff onsite in the hotel. They were able to organise and provide the training in a timely manner as usual. We always find SafeHands very reliable from start to finish.”
Lessons for Irish Businesses Evaluating Digital Transformation
SafeHands’ sustained success offers several lessons for Irish businesses considering which services to digitise:
Question Default Assumptions: Just because services can be delivered digitally doesn’t mean they should be. Evaluate whether digital delivery genuinely improves outcomes or merely reduces costs.
Consider Competitive Positioning: Services that everyone digitises become commoditised quickly. Maintaining physical delivery where it adds genuine value can create differentiation.
Value Operational Excellence: Complex operations executed well create competitive advantages that simple digital platforms cannot easily replicate.
Build for Retention: Digital platforms optimise for acquisition. Physical service models can optimise for long-term relationships that generate better unit economics over time.
Use Technology Strategically: Adopt digital tools where they solve real problems (payment processing, scheduling) whilst keeping core service delivery in whatever format creates the most value.
The Countertrend Opportunity
As more services migrate online, opportunities emerge for businesses willing to deliver excellent physical service. Markets become less crowded. Clients willing to pay premium prices for superior outcomes become easier to reach. Competitive differentiation becomes simpler.
Michael Mongan from The Lovely Food Co in Dublin praised the hands-on approach: “SafeHands Health & Safety Solutions delivered a Food Safety/HACCP Level 2 Course onsite at our premises recently. Our staff really enjoyed the training session and had great praise for the SafeHands instructor and his very comprehensive food safety knowledge.”
The phrase “really enjoyed” seems unusual for compliance training—until you recognise that well-delivered, contextually relevant, hands-on instruction creates genuinely valuable experiences that generic online courses cannot match.
Conclusion: Digital-First Isn’t Always Best-First
The lesson from SafeHands’ decade of success isn’t that digital transformation is wrong. It’s that strategic thinking matters more than following trends.
Some services work better digitally. Others work better physically. Many benefit from hybrid approaches combining both. The key is honest evaluation of where value actually gets created rather than defaulting to digital simply because that’s the current consensus.
For Irish businesses evaluating their own service delivery models, the question isn’t “Should we go digital?” It’s “For which specific services does digital delivery improve outcomes, and for which does it merely reduce our costs whilst degrading client experience?”
SafeHands demonstrates that choosing the harder operational path—when it genuinely serves clients better—can build sustainable competitive advantages that easier digital alternatives cannot replicate.
SafeHands Health & Safety Solutions has operated across Ireland since 2013, demonstrating that strategic service delivery decisions matter more than following industry trends. Their sustained client relationships and consistent growth show that “digital-first” isn’t always “best-first” for businesses focused on genuine value creation.
Ireland’s gaming scene has grown into something far bigger than casual entertainment. It is now a fast-moving mix of creativity, technology, and finance, and it is still expanding. From indie studios in Galway to global publishers with Dublin offices, the country’s footprint in the industry keeps getting stronger. But with growth comes a new challenge: how do you make money from games in ways that keep players engaged and coming back? The future of monetisation here is not just about revenue; it is about building systems that feel seamless, smart, and worth investing in.
From One-Off Purchases to Ongoing Revenue
The way games make money has completely changed in the past decade. Buying a title once and playing it for years has given way to microtransactions, subscription models, and in-game purchases that keep evolving with the game itself.
Ireland has followed the global shift to digital platforms and seamless payments, but with a stronger emphasis on trust and transparency. As iGaming continues to evolve, titles that could be found on exclusive Inclave casinos by pokerscout.com show how integrated gameplay, community features, and built-in payment options can create engaging, repeatable experiences across thousands of games where players can also enjoy exciting bonuses. The same as major releases like Fortnite, where in-game economies, events, and microtransactions are now central to how players interact with the game itself. These examples show how the future of monetisation will revolve around ecosystems that blend play, connection, and payment into something players want to keep returning to.
Clearer Rules, More Confidence
Game-related monetisation in Ireland is entering a new phase, with clearer boundaries emerging around areas like loot boxes, virtual currencies, and skill-based competitions, all while leaving room for creativity and new ideas. This shift is creating a more stable environment for studios to test different models without second-guessing how they will land.
For players, it means more consistent, transparent experiences and greater confidence in how games are structured and paid for. That balance between innovation and clarity will be what pushes Ireland forward as one of Europe’s most dynamic gaming markets, building an industry where fresh ideas can thrive and audiences feel valued.
New Monetisation Models Taking Shape
Subscription-based access is on the rise, with services like Game Pass and PlayStation Plus proving that players value choice and flexibility. Blockchain and digital wallets are also opening up new ways to manage in-game assets, and while NFTs themselves have cooled off, the technology behind them still holds a lot of potential.
Skill-based competitions and real-money tournaments are growing too, especially among Ireland’s tech-savvy audiences. In every case, trust and usability are what drive spending, and platforms like Inclave are already showing how that combination works in practice.
Where Gaming and Fintech Meet
Ireland’s strength as a tech and fintech hub gives it a serious edge. The same systems that are driving modern finance are fuelling the next generation of gaming. Start-ups in Dublin, Cork, and Limerick are already exploring hybrid models that blend interactive entertainment with financial technology, backed by Enterprise Ireland and EU funding.
These projects are looking beyond traditional payment methods, exploring integrated wallets, real-time rewards, and shared-value systems that make spending feel like part of the experience rather than a barrier to it. It’s easy to imagine Ireland becoming a testing ground for new ways to pay for games, approaches that focus less on one-off purchases and more on rewarding loyalty, building communities, and keeping engagement high.
Evolving Player Expectations
Irish players are increasingly mobile-first, switching between phones, tablets, and cloud platforms rather than relying solely on consoles or PCs. That shift demands monetisation models designed for flexibility and smaller, more frequent interactions rather than big upfront costs. It also changes how games are designed in the first place, shorter sessions, seamless cross-device play, and features that work just as well on the move as they do at home are becoming essential. Payment systems are evolving too, with integrated wallets, instant purchases, and subscription-style access built to match how people now play. The focus is shifting from single, high-value sales to ongoing engagement that fits naturally into daily life.
There is a growing expectation for clarity and simplicity; players want transparent pricing, clear communication about what they are paying for, and rewards that feel meaningful. They are looking to be part of a game’s evolution, not just passive buyers. Data analytics, personalisation, and loyalty systems will all shape how Irish studios build deeper connections with their audiences.
Looking Ahead
The future of game monetisation in Ireland depends on the right mix of creativity, technology, and clear frameworks. Developers are pushing for the freedom to experiment, players are looking for platforms they can rely on, and the industry is steadily moving towards revenue models that feel smarter and more seamless.
With secure systems like those seen in the Inclave network already leading the way, Ireland is well-positioned to shape the next phase of gaming. Whether through subscriptions, skill-based play, or blockchain-powered systems, the Irish market is set to redefine how games are valued not just in euros, but in how much players want to be part of them.
Today, Google is rolling out AI Mode, Google’s most powerful AI search experience, in Ireland. AI Mode has more advanced reasoning and multimodality, and the ability to go deeper through follow-up questions and helpful links to the web. Whether someone is looking for products, planning a trip, or exploring a new topic, AI Mode is designed to understand what they’re really asking for and help them to find it. From today AI mode will appear as a tab on the Search results page and in the Google app for Android and iOS.
AI Mode is a new, intuitive way to address the most complex, multi-part questions and follow-ups on Search, and satisfy people’s curiosity in a richer way. Using a custom version of Google’s advanced Gemini models for Search, it lets people ask nuanced questions that would have previously required multiple searches. It is particularly helpful for exploratory questions and for more complicated tasks like planning a trip or understanding complex how-tos. It’s even designed to be truly multimodal, so questions can be asked in whatever way feels most natural — whether that’s with text, voice or even camera. Google has found that early users of AI Mode are asking questions that are two or three times the length of traditional search queries.
As autumn arrives and people begin to turn to warmer clothes, Google Search Trends data shows that ‘How to style a big cardigan’ is the most searched ‘How to style’ question in Ireland in the last 30 days. For those looking for styling help, AI mode can act as a personal stylist and help with styling ideas including showing different outfits that incorporate a large cardigan. Coffee lovers can ask questions like, ‘I’m looking to understand different coffee brewing methods. Make a table comparing the differences in taste, ease of use and equipment needed.’ And even follow up with: ‘what’s the best grind size for each method’.
As Google continues to evolve the Search experience with AI capabilities, the focus remains on helping people access information and perspectives from a diverse range of sources. For example, with AI Overviews, people have been visiting a greater diversity of websites for help with more complex questions. And when people click from search result pages with AI Overviews, these clicks are higher quality for websites — meaning users are more likely to spend more time on the sites they visit.
AI Mode is rooted in Google’s core quality and ranking systems, and novel approaches are being used to improve factuality. Google aims to show an AI-powered response as much as possible, but in cases where there isn’t high confidence, users will see a set of web search results. As with any early-stage AI product, it won’t always be right, but Google is committed to continuous improvement.
Remember when the most valuable thing you could own in a video game was a high score? Those days are long gone. Today, a vibrant, multi-billion-dollar economy thrives within our favorite games, powered by digital goods, everything from a fancy sword for your elf warrior to a limited-edition character skin in a shooter game. But this isn’t just about looking cool; it’s about a massive shift in how we view and value our digital possessions.
From pixelated swords to valuable assets
It started with a simple idea: customization. Games like Team Fortress 2 introduced hats and other cosmetic items that let players express their individuality. But when these items became rare, something interesting happened: they became desirable. And where there’s desire, an economy is born.
Developers quickly realized that players were willing to spend real money not just on the game itself but on items within the game. This led to the creation of official in-game stores. But the real revolution began when players wanted to trade these items with each other.
What are some marketplace models?
Not all marketplaces are created equal. They generally fall into two main categories, each with its own pros and cons. The most powerful driver for third-party sites is their ability to turn your CSGO skins into real money whenever you need it and for the best price. This concept of “cashing out” blurred the line between the digital and physical economies, making virtual goods feel like genuine, tangible assets.
Feature
Official marketplaces
Third-party marketplaces
Security
Very high. Integrated directly with the game.
Variable. Reputable sites are secure, but scams exist.
Fees
High. The game publisher takes a significant cut.
Lower. Typically lower fees than official platforms.
Flexibility
Low. Often locked to in-game credit (e.g., Steam Wallet).
High. Often allows cashing out to real-world money.
Item availability
Limited to what the publisher sells or allows.
Vast. A huge range of items from countless players.
Why do these marketplaces work?
So, why have these digital flea markets exploded in popularity?
Player Expression: In a world of millions of players, a rare skin or emote is a badge of honor. It’s a way to stand out and show your dedication.
Perceived Value and Scarcity: Just like a rare trading card or a limited-edition sneaker, digital items gain value when they are hard to get. Limited-time offers and rarity tiers create a powerful sense of scarcity.
The Thrill of the Hunt: Opening a “loot box” or crafting a new item triggers a dopamine rush similar to gambling. Marketplaces tap into this excitement by letting players buy, sell, and trade that thrill.
Community and Status: Owning a coveted item isn’t just about the item itself; it’s about the social status it grants you within your gaming community.
What is the future of digital goods?
This economy is only getting bigger and more complex. Here’s what’s on the horizon:
The Blockchain and NFTs: Some games are already experimenting with putting true ownership of digital items on the blockchain via NFTs. This could make items truly unique, verifiable, and interoperable across different games.
The Metaverse: As concepts of a persistent, interconnected virtual world develop, the digital goods you buy in one game or experience could be used in another.
Stronger Regulation: With real money involved, governments are taking notice. We can expect more regulations around loot boxes, taxation of digital asset sales, and consumer protection.
The rise of gaming marketplaces is more than a trend; it’s a fundamental change in the relationship between players and the games they love. Our digital closets are now showcases of identity, history, and sometimes, significant investment. These marketplaces have given pixels a price tag and created a thrilling, complex, and entirely new layer to the world of gaming.
Equifax has announced the opening of its state-of-the-art AI Innovation Lab at its operation in Wexford, Ireland, a new facility dedicated to advancing the company’s global artificial intelligence research and development. The new Lab builds on the company’s more than 10-year history of AI innovation and expands its global team of over 1,200 data and analytics professionals.
This investment, supported by the Irish Government, through IDA Ireland, will serve as a global hub for innovation, bringing together highly skilled data and technology specialists to create next-generation AI solutions that enhance decision-making, improve customer and consumer experiences, and help global organisations reduce risk.
The AI Innovation Lab will focus on developing advanced AI models, machine learning algorithms, Research and Development, and data analytics tools to address complex challenges faced by businesses and consumers. For its initial phase, the lab will concentrate on AI-driven solutions for credit risk assessment that can augment decision-making for fintechs and financial institutions. These innovations will leverage advanced AI to provide affordability and creditworthiness insights, helping more people access mainstream financial opportunities and driving greater financial inclusion. This includes enhancing fraud detection, improving credit risk assessment, optimising marketing strategies, and strengthening cybersecurity measures.
The lab will play an important role in advancing the Equifax global EFX.AI strategy, furthering the company’s ongoing innovation and the development of data-driven solutions that can help open up new financial opportunities for consumers.
Ceann Comhairle, Verona Murphy TD, said:“The Equifax decision to establish its AI Innovation Lab in Wexford is a powerful vote of confidence in Wexford and the South East Region, this investment highlights the highly skilled workforce and culture of innovation on offer in Wexford for companies that wish to expand their offering globally.”
Paul Heywood, Chief Data & Analytics Officer for Equifax Europe, commented:“Our new AI Innovation Lab is designed to empower financial institutions with the tools needed to make smarter, faster, and more confident credit decisions, helping more consumers live their financial best. Through the AI Innovation Lab, and by harnessing our unique data and insights within the Equifax Cloud, we are committed to delivering secure, reliable, and innovative best-in-class solutions for businesses and consumers alike.
Driving AI innovation is a key EFX2027 Strategic Priority. We are incredibly excited to expand our Wexford team through this investment and have immense confidence in the calibre and expertise of the marketplace in Ireland and specifically in the South East, as evidenced by our thirty-year presence in Wexford town.”
Deirdre O’Connor, Head of Regional Development, IDA Ireland said: ‘I wish to congratulate Equifax on the opening of this AI Innovation lab. Artificial Intelligence is a key growth driver in IDA Ireland’s new strategy ‘Adapt Intelligently.’ The Equifax decision to expand its presence in Wexford is a testament to Ireland’s position as a hub for global innovation and AI. I am delighted that this will bring Equifax employment in Wexford to 100 employees and would like to wish them every success with this AI Innovation Lab.’’
The official opening on 10 September 2025 brought together senior Equifax leaders, Raghu Kulkarni, Chief AI Officer; Ritu Sharma, SVP, Global AI Governance and Model Risk Management; Elizabeth Chapman, VP Operations, Transformation and Change; and Paul Heywood, Chief Data and Analytics Officer for Equifax UK; as well as government representatives, and industry stakeholders including IDA Ireland.
