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.

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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.

The post Martin Frederik, Snowflake: Data quality is key to AI-driven growth appeared first on AI News.