An operational AI forecasting model developed by Hertfordshire University researchers aims to improve resource efficiency within healthcare.

Public sector organisations often hold large archives of historical data that do not inform forward-looking decisions. A partnership between the University of Hertfordshire and regional NHS health bodies addresses this issue by applying machine learning to operational planning. The project analyses healthcare demand to assist managers with decisions regarding staffing, patient care, and resources.

Most AI initiatives in healthcare focus on individual diagnostics or patient-level interventions. The project team notes that this tool targets system-wide operational management instead. This distinction matters for leaders evaluating where to deploy automated analysis within their own infrastructure.

The model uses five years of historical data to build its projections. It integrates metrics such as admissions, treatments, re-admissions, bed capacity, and infrastructure pressures. The system also accounts for workforce availability and local demographic factors including age, gender, ethnicity, and deprivation.

Iosif Mporas, Professor of Signal Processing and Machine Learning at the University of Hertfordshire, leads the project. The team includes two full-time postdoctoral researchers and will continue development through 2026.

“By working together with the NHS, we are creating tools that can forecast what will happen if no action is taken and quantify the impact of a changing regional demographic on NHS resources,” said Professor Mporas.

Using AI for forecasting in healthcare operations

The model produces forecasts showing how healthcare demand is likely to change. It models the impact of these changes in the short-, medium-, and long-term. This capability allows leadership to move beyond reactive management.

Charlotte Mullins, Strategic Programme Manager for NHS Herts and West Essex, commented: “The strategic modelling of demand can affect everything from patient outcomes including the increased number of patients living with chronic conditions.

“Used properly, this tool could enable NHS leaders to take more proactive decisions and enable delivery of the 10-year plan articulated within the Central East Integrated Care Board as our strategy document.” 

The University of Hertfordshire Integrated Care System partnership funds the work, which began last year. Testing of the AI model tailored for healthcare operations is currently underway in hospital settings. The project roadmap includes extending the model to community services and care homes.

This expansion aligns with structural changes in the region. The Hertfordshire and West Essex Integrated Care Board serves 1.6 million residents and is preparing to merge with two neighbouring boards. This merger will create the Central East Integrated Care Board. The next phase of development will incorporate data from this wider population to improve the predictive accuracy of the model.

The initiative demonstrates how legacy data can drive cost efficiencies and shows that predictive models can inform “do nothing” assessments and resource allocation in complex service environments like the NHS. The project highlights the necessity of integrating varied data sources – from workforce numbers to population health trends – to create a unified view for decision-making.

See also: Agentic AI in healthcare: How Life Sciences marketing could achieve $450B in value by 2028

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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Generative AI’s experimental phase is concluding, making way for truly autonomous systems in 2026 that act rather than merely summarise.

2026 will lose the focus on model parameters and be about agency, energy efficiency, and the ability to navigate complex industrial environments. The next twelve months represent a departure from chatbots toward autonomous systems executing workflows with minimal oversight; forcing organisations to rethink infrastructure, governance, and talent management.

Autonomous AI systems take the wheel

Hanen Garcia, Chief Architect for Telecommunications at Red Hat, argues that while 2025 was defined by experimentation, the coming year marks a “decisive pivot towards agentic AI, autonomous software entities capable of reasoning, planning, and executing complex workflows without constant human intervention.”

Telecoms and heavy industry are the proving grounds. Garcia points to a trajectory toward autonomous network operations (ANO), moving beyond simple automation to self-configuring and self-healing systems. The business goal is to reverse commoditisation by “prioritising intelligence over pure infrastructure” and reduce operating expenditures.

Technologically, service providers are deploying multiagent systems (MAS). Rather than relying on a single model, these allow distinct agents to collaborate on multi-step tasks, handling complex interactions autonomously. However, increased autonomy introduces new threats.

Emmet King, Founding Partner of J12 Ventures, warns that “as AI agents gain the ability to autonomously execute tasks, hidden instructions embedded in images and workflows become potential attack vectors.” Security priorities must therefore shift from endpoint protection to “governing and auditing autonomous AI actions.”

As organisations scale these autonomous AI workloads, they hit a physical wall: power.

King argues energy availability, rather than model access, will determine which startups scale. “Compute scarcity is now a function of grid capacity,” King states, suggesting energy policy will become the de facto AI policy in Europe.

KPIs must adapt. Sergio Gago, CTO at Cloudera, predicts enterprises will prioritise energy efficiency as a primary metric. “The new competitive edge won’t come from the largest models, but from the most intelligent, efficient use of resources.”

Horizontal copilots lacking domain expertise or proprietary data will fail ROI tests as buyers measure real productivity. The “clearest enterprise ROI” will emerge from manufacturing, logistics, and advanced engineering—sectors where AI integrates into high-value workflows rather than consumer-facing interfaces.

AI ends the static app in 2026

Software consumption is changing too. Chris Royles, Field CTO for EMEA at Cloudera, suggests the traditional concept of an “app” is becoming fluid. “In 2026, AI will start to radically change the way we think about apps, how they function and how they’re built.”

Users will soon request temporary modules generated by code and a prompt, effectively replacing dedicated applications. “Once that function has served its purpose, it closes,” Royles explains, noting these “disposable” apps can be built and rebuilt in seconds.

Rigorous governance is required here; organisations need visibility into the reasoning processes used to create these modules to ensure errors are corrected safely.

Data storage faces a similar reckoning, especially as AI becomes more autonomous. Wim Stoop, Director of Product Marketing at Cloudera, believes the era of “digital hoarding” is ending as storage capacity hits its limit.

“AI-generated data will become disposable, created and refreshed on demand rather than stored indefinitely,” Stoop predicts. Verified, human-generated data will rise in value while synthetic content is discarded.

Specialist AI governance agents will pick up the slack. These “digital colleagues” will continuously monitor and secure data, allowing humans to “govern the governance” rather than enforcing individual rules. For example, a security agent could automatically adjust access permissions as new data enters the environment without human intervention.

Sovereignty and the human element

Sovereignty remains a pressing concern for European IT. Red Hat’s survey data indicates 92 percent of IT and AI leaders in EMEA view enterprise open-source software as vital for achieving sovereignty. Providers will leverage existing data centre footprints to offer sovereign AI solutions, ensuring data remains within specific jurisdictions to meet compliance demands.

Emmet King, Founding Partner of J12 Ventures, adds that competitive advantage is moving from owning models to “controlling training pipelines and energy supply,” with open-source advancements allowing more actors to run frontier-scale workloads.

Workforce integration is becoming personal. Nick Blasi, Co-Founder of Personos, argues tools ignoring human nuance – tone, temperament, and personality – will soon feel obsolete. By 2026, Blasi predicts “half of workplace conflict will be flagged by AI before managers know it exists.”

These systems will focus on “communication, influence, trust, motivation, and conflict resolution,” Blasi suggests, adding that personality science will become the “operating system” for the next generation of autonomous AI, offering grounded understanding of human individuality rather than generic recommendations.

The era of the “thin wrapper” is over. Buyers are now measuring real productivity, exposing tools built on hype rather than proprietary data. For the enterprise, competitive advantage will no longer come from renting access to a model, but from controlling the training pipelines and energy supply that power it.

See also: BBVA embeds AI into banking workflows using ChatGPT Enterprise

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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OpenAI has completed a major reorganisation and, in the same breath, signed a new definitive partnership agreement with Microsoft.

Starting with OpenAI’s reorganisation, the aim is to solidify the nonprofit’s control over the for-profit business and establish the newly named OpenAI Foundation as a global philanthropic powerhouse, holding equity in the commercial arm valued at approximately $130 billion.

This reorganisation, which OpenAI says “maintains the strongest representation of mission-focused governance in the industry today,” effectively turns the company’s commercial success into a direct funding pipeline for its original mission.

The for-profit entity is now a public benefit corporation called OpenAI Group PBC, legally bound to that mission. As this PBC grows, so does the Foundation’s $130 billion stake, which will be used to fund an initial $25 billion commitment to global health and AI resilience.

This restructure was finalised after nearly a year of “constructive dialogue” with the offices of the Attorneys General of California and Delaware. OpenAI acknowledged it “made several changes as a result of those discussions” and stated its belief that the company, and by extension the public it serves, “are better for them.”

The other side of this new structure is the redefined partnership with Microsoft. The tech giant’s investment is now valued at $135 billion, giving it a 27 percent stake in the OpenAI Group PBC. This represents a slight dilution from its previous 32.5 percent stake, reflecting new funding rounds. The agreement preserves Microsoft’s core position as the exclusive Azure API provider for OpenAI’s frontier models, but only until artificial general intelligence (AGI) is achieved.

The new terms introduce a new check on that path. Any declaration of AGI by OpenAI must now be verified by an independent expert panel. This external check is a major update to the governance of the partnership. Microsoft’s intellectual property rights are also extended through 2032 and now include models developed after AGI is declared, with appropriate safety guardrails.

Microsoft can also now independently pursue AGI, either on its own or with other partners. This gives Microsoft a new path forward, separate from its reliance on OpenAI’s research. If Microsoft uses OpenAI’s IP to develop AGI before it is officially declared, those models will be subject to compute thresholds significantly larger than systems in use today.

But the new freedoms cut both ways. OpenAI has also secured new flexibility. It has committed to purchasing an incremental $250 billion of Azure services, but Microsoft no longer holds a right of first refusal as its compute provider. This gives OpenAI new leverage in its infrastructure negotiations.

The company can also now release open weight models that meet certain criteria and serve US government national security customers on any cloud, a notable new ability. It also gains the power to jointly develop some non-API products with third parties, although API products developed with others must remain on Azure. Microsoft’s IP rights also specifically exclude any of OpenAI’s future consumer hardware.

The existing revenue share agreement remains in place until the expert panel verifies AGI, though payments will be stretched over a longer period. Both companies framed the new chapter as a way to continue innovating. OpenAI concluded that this new structure provides both the ability to push the AI frontier and an updated model to “ensure that progress serves everyone.”

See also: OpenAI connects ChatGPT to enterprise data to surface knowledge

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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Google has announced DeepSomatic, an AI tool that can identify cancer-related mutations in tumour genetic sequences more accurately.

Cancer starts when the controls governing cell division malfunction. Finding the specific genetic mutations driving a tumour’s growth is essential for creating effective treatment plans. Doctors now regularly sequence tumour cell genomes from biopsies to inform treatments that can target how a particular cancer grows and spreads.

Published in Nature Biotechnology, this work presents a tool that uses convolutional neural networks to identify genetic variants in tumour cells with greater accuracy than current methods. Google has made both DeepSomatic and the high-quality training dataset created for it openly available.

The challenge of somatic variants

Cancer genetics is complex. While genome sequencing finds genetic cancer variations, distinguishing real variants from sequencing errors is difficult and where an AI tool would provide welcome assistance. Most cancers are driven by ‘somatic’ variants acquired after birth rather than inherited ‘germline’ variants from parents.

Somatic mutations happen when environmental factors like UV light damage DNA, or when random errors occur during DNA replication. When these variants alter normal cell behaviour, they can cause uncontrolled replication, driving cancer development and progression.

Identifying somatic variants is harder than finding inherited ones because they can exist at low frequencies within tumour cells, sometimes at rates lower than the sequencing error rate itself.

How DeepSomatic works

In clinical settings, scientists sequence both tumour cells from a biopsy and normal cells from the patient. DeepSomatic spots the differences, identifying variations in tumour cells that aren’t inherited. These variations reveal what’s fuelling the tumour’s growth.

The model converts raw genetic sequencing data from both tumour and normal samples into images representing various data points, including the sequencing data and its alignment along the chromosome. A convolutional neural network analyses these images to differentiate between the standard reference genome, the individual’s normal inherited variants, and cancer-causing somatic variants while filtering out sequencing errors. The output is a list of cancer-related mutations.

DeepSomatic can also work in ‘tumour-only’ mode when normal cell samples are unavailable, which happens frequently with blood cancers like leukaemia. This makes the tool applicable across many research and clinical scenarios.

Training a more precise AI cancer research tool

Training an accurate AI model requires high-quality data. For its AI tool, Google and its partners at the UC Santa Cruz Genomics Institute and the National Cancer Institute created a benchmark dataset called CASTLE. They sequenced tumour and normal cells from four breast cancer samples and two lung cancer samples.

These samples were analysed using three leading sequencing platforms to create a single, accurate reference dataset by combining the outputs and removing platform-specific errors. The data shows how even the same cancer type can have vastly different mutational signatures, information that can help predict patient response to specific treatments.

DeepSomatic models performed better than other established methods across all three major sequencing platforms. The tool excelled at identifying complex mutations called insertions and deletions, or ‘Indels’. For these variants, DeepSomatic achieved a 90% F1-score on Illumina sequencing data, compared to 80% for the next-best method. The improvement was more dramatic on Pacific Biosciences data, where DeepSomatic scored over 80% while the next-best tool scored less than 50%.

The AI performed well when analysing challenging samples. Testing included a breast cancer sample preserved with formalin-fixed-paraffin-embedded (FFPE), a common method that can introduce DNA damage and complicate analysis. It was also tested on data from whole exome sequencing (WES), a more affordable method that sequences only the 1% of the genome coding for proteins. In both scenarios, DeepSomatic outperformed other tools, suggesting its utility for analysing lower-quality or historical samples.

An AI tool for all cancers

The AI tool has shown it can apply its learning to new cancer types it wasn’t trained on. When used to analyse a glioblastoma sample, an aggressive brain cancer, it successfully pinpointed the few variants known to drive the disease. In a partnership with Children’s Mercy in Kansas City, it analysed eight samples of paediatric leukaemia and found the previously known variants while identifying 10 new ones, despite working with tumour-only samples.

Google hopes research labs and clinicians will adopt this tool to better understand individual tumours. By detecting known cancer variants, it could help guide choices for existing treatments. By identifying new ones, it could lead to new therapies. The goal is to advance precision medicine and deliver more effective treatments to patients.

See also: MHRA fast-tracks next wave of AI tools for patient care

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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Boston Consulting Group (BCG) has found a widening chasm separating an elite of AI masters from the majority of firms struggling to generate any value from their AI investments.

A study from BCG found that a mere five percent of companies are successfully achieving bottom-line value from AI at scale. In sharp contrast, 60 percent are failing to achieve any material value, reporting only minimal gains despite making substantial investments in the technology.

“AI is reshaping the business landscape far faster than previous technology waves,” said Nicolas de Bellefonds , a managing director and senior partner and global leader of BCG’s AI efforts, and a coauthor of the report.

“The companies that are capturing real value from AI aren’t just automating—they’re reshaping and reinventing how their businesses work. And they’re pulling away.”

Top-performing organisations, which BCG labels “future-built,” aren’t just succeeding; they are creating a formidable and widening AI value gap. They already generate 1.7 times more revenue growth and 1.6 times higher EBIT margins than the lagging majority. This elite group has moved beyond isolated experiments to fundamentally reinvent their operations, driving shareholder returns through revenue increases and measurable workflow improvements. The remaining 35 percent of companies are making efforts to scale up but admit they are not moving fast enough to keep pace.

Future-built companies, having reaped early rewards, are now reinvesting their gains to pull even further ahead. They plan to spend 26 percent more on IT and dedicate 64 percent more of their IT budget to AI in 2025. This results in an overall AI investment that is 120 percent higher than their slower competitors.

As a consequence, future-built companies expect to see double the revenue increases and 1.4 times greater cost reductions from their AI applications. For the laggards, who lack foundational capabilities and generate almost no value, this creates what BCG calls a “vicious cycle of losing ground.”

A key reason for this disparity is a failure of leadership. Among lagging firms, top management often delegates AI strategy to middle or lower management, fails to articulate a clear vision for value from investments, and spreads resources too thinly across disconnected initiatives.

The secret to success lies in a proven playbook followed by the leading five percent. These firms approach AI as a board and CEO-sponsored multiyear programme with ambitious, clearly defined targets. 

Nearly all C-level leaders in future-built organisations are deeply engaged with AI, compared to only eight percent in lagging companies. They foster a model of shared ownership between business and IT departments, a practice they are 1.5 times more likely to adopt than their peers. One senior retail executive told BCG they “concentrate in particular on senior sponsorship and ownership of AI benefits by the businesses, which creates the room to invest.”

These leaders are not merely automating existing processes. They focus on reshaping and inventing core business workflows where the majority of value lies. The report found that 70 percent of AI’s potential value is concentrated in core functions such as R&D, sales, marketing, and manufacturing. Future-built companies prioritise this reinvention, resulting in 62 percent of their AI initiatives already being deployed, compared to just 12 percent for the laggards.

An accelerator of the value gap is the emergence and investment in agentic AI – which combines predictive and generative capabilities – allowing it to “reason, learn, and act autonomously” with minimal human input. These AI agents can be seen as digital workers, capable of handling complex workflows from supply chain management to customer service.

While hardly discussed in 2024, agentic AI already accounts for 17 percent of total AI value in 2025 and is projected to almost double to 29 percent by 2028. The top firms are moving quickly, with a third already using agents, compared to almost none of the laggards. These leaders are prioritising customer experience use cases for agents, with customer service being the top focus for 50 percent of companies.

“Agentic AI isn’t a future concept—it’s already reshaping workflows and redefining roles. Companies should view it as the next step in scaling AI, not as the starting point,” said Amanda Luther , a managing director and senior partner at BCG and a coauthor of the report.

“Agents represent a huge opportunity but aren’t simply plug-and-play: companies urgently need to redesign how work gets done, addressing the impact of agents on existing processes, roles, and skills.”

Talent is another key differentiator. Rather than focusing on job losses, future-built companies are aggressively upskilling their workforce to collaborate with AI. They plan to upskill more than 50 percent of their internal staff, making investments in broad-based employee AI enablement and carving out dedicated time for structured learning. This approach is six times more likely than in lagging companies. They also involve employees twice as often in the process of co-designing and reshaping workflows to incorporate AI agents, ensuring smoother adoption and building trust.

Leading organisations avoid the “GenAI burden” of siloed, unscalable proofs-of-concept by building on a central, integrated AI platform. They are three times more likely to operate such a platform, allowing them to build common capabilities for security and monitoring just once and then reuse them, accelerating deployment and ensuring enterprise-wide scale. More than half of these firms operate on a single, enterprise-wide data model, compared to just four percent of their stagnating peers, giving teams quick access to reliable and governed data.

For the 95 percent of companies falling behind, the message is urgent. The path to success is clearly delineated, but it requires a fundamental shift in mindset and organisation. BCG advises following a “10-20-70 rule,” where transformation efforts should focus 70 percent on people and processes, 20 percent on technology, and only 10 percent on the algorithms themselves.

The biggest roadblocks to achieving value from AI investments are not technical but organisational, relating to people, strategy, and processes. As the technology advances and the leaders accelerate, the window for catching up is closing fast. Firms that fail to act decisively now risk being permanently left behind.

See also: Samsung benchmarks real productivity of enterprise AI models

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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As companies race to implement AI, many are finding that project success hinges directly on the quality of their data. This dependency is causing many ambitious initiatives to stall, never making it beyond the experimental proof-of-concept stage.

So, what’s the secret to turning these experiments into real revenue generators? AI News caught up with Martin Frederik, regional leader for the Netherlands, Belgium, and Luxembourg at data cloud giant Snowflake, to find out.

“There’s no AI strategy without a data strategy,” Frederik says simply. “AI apps, agents, and models are only as effective as the data they’re built on, and without unified, well-governed data infrastructure, even the most advanced models can fall short.”

Improving data quality is key to AI project success

It’s a familiar story for many organisations: a promising proof-of-concept impresses the team but never translates into a tool that makes the company money. According to Frederik, this often happens because leaders treat the technology as the end goal.

“AI is not the destination – it’s the vehicle to achieving your business goals,” Frederik advises.

When projects get stuck, it’s usually down to a few common culprits: the project isn’t truly aligned with what the business needs, teams aren’t talking to each other, or the data is a mess. It’s easy to get disheartened by statistics suggesting that 80% of AI projects don’t reach production, but Frederik offers a different perspective. This isn’t necessarily a failure, he suggests, but “part of the maturation process”.

For those who get the foundation right, the payoff is very real. A recent Snowflake study found that 92% of companies are already seeing a return on their AI investments. In fact, for every £1 spent, they’re getting back £1.41 in cost savings and new revenue. The key, Frederik repeats, is having a “secure, governed and centralised platform” for your data from the very beginning.

It’s not just about tech, it’s about people

Even with the best technology, an AI strategy can fall flat if the company culture isn’t ready for it. One of the biggest challenges is getting data into the hands of everyone who needs it, not just a select few data scientists. To make AI work at scale, you have to build strong foundations in your “people, processes, and technology.”

This means breaking down the walls between departments and making quality data and AI tools accessible to everyone.

“With the right governance, AI becomes a shared resource rather than a siloed tool,” Frederik explains. When everyone works from a single source of truth, teams can stop arguing about whose numbers are correct and start making faster and smarter decisions together.

The next leap: AI that reasons for itself

The true breakthrough we’re seeing now is the emergence of AI agents that can understand and reason over all kinds of data at once regardless of structure quality; from the neat rows and columns in a spreadsheet, to the unstructured information in documents, videos, and emails. Considering that this unstructured data makes up 80-90% of a typical company’s data, this is a huge step forward.

New tools are enabling staff, no matter their technical skill level, to simply ask complex questions in plain English and get answers directly from the data.

Frederik explains that this is a move towards what he calls “goal-directed autonomy”. Until now, AI has been a helpful assistant you had to constantly direct. “You ask a question, you get an answer; you ask for code, you get a snippet,” he notes.

The next generation of AI is different. You can give an agent a complex goal, and it will figure out the necessary steps on its own, from writing code to pulling in information from other apps to deliver a complete answer. This will automate the most time-consuming parts of a data scientist’s job, like “tedious data cleaning” and “repetitive model tuning.”

The result? It frees up your brightest minds to focus on what really matters. This elevates your people “from practitioner to strategist” and allows them to drive real value for the business. That can only be a good thing.

Snowflake is a key sponsor of this year’s AI & Big Data Expo Europe and will have a range of speakers sharing their deep insights during the event. Swing by Snowflake’s booth at stand number 50 to hear more from the company about making enterprise AI easy, efficient, and trusted.

See also: Public trust deficit is a major hurdle for AI growth

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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