Nature, Published online: 03 October 2025; doi:10.1038/d41586-025-03246-7

Researchers are increasingly turning to artificial-intelligence tools that can handle complex, multi-step processes.

Nature, Published online: 03 October 2025; doi:10.1038/d41586-025-03201-6

Machine learning can speed up the discovery of potential antibiotics, but challenges remain.

Nature Medicine, Published online: 30 September 2025; doi:10.1038/s41591-025-03933-y

In a prospective study enrolling 1,222 patients from 22 emergency departments, a device using a machine-learning-based signature of blood mRNAs demonstrated clinically acceptable performance to diagnose bacterial and viral infections and to predict the all-cause need for critical care interventions within 7 days, with benchmark to established biomarkers and risk scores.

When cancer cells are physically squeezed, they mount an instant, high-energy defense by rushing mitochondria to the cell nucleus, unleashing a surge of ATP that fuels DNA repair and survival. This newly discovered mechanism, visualized in real time with advanced microscopy, shows mitochondria acting like emergency first responders rather than static power plants. The structures, called NAMs, were also identified in patient tumor biopsies, suggesting real-world relevance to cancer’s spread.

Nature, Published online: 01 October 2025; doi:10.1038/d41586-025-03098-1

Priorities for medical research depend on a country’s stage of development and the money available.

Guest author: Or Hillel, Green Lamp

Applications have become the foundation of how organisations deliver services, connect with customers, and manage important operations. Every transaction, interaction, and workflow runs on a web app, mobile interface, or API. That central role has made applications one of the most attractive and frequently-targeted points of entry for attackers.

As software grows more complex, spanning microservices, third-party libraries, and AI-powered functionality, so do the security risks. Traditional scanning methods struggle to keep up with rapid release cycles and distributed architectures. This has opened the door for AI-driven application security tools, which bring automation, pattern recognition, and predictive capabilities to a field that once relied heavily on manual reviews and static checks.

Best practices for using AI AppSec tools

To get the most value from AI-powered application security, teams should follow some key best practices:

1. Shift security left: Integrate tools early in the SDLC so issues are caught before production.
2. Combine approaches: Use AI tools alongside traditional SAST, DAST, and manual reviews to cover all bases.
3. Enable continuous learning: Choose solutions that improve over time by ingesting threat intelligence and user feedback.
4. Keep humans in the loop: AI should augment, not replace, human judgment. Security experts are still needed for complex decision-making.
5. Align with compliance: Ensure AI-powered findings can be mapped to regulatory requirements like SOC 2, HIPAA, or GDPR.

The 5 best AI-powered AppSec tools of 2025

1. Apiiro

Apiiro is reinventing the way organisations assess and manage risk in the modern software supply chain. It moves beyond legacy scanning to implement true risk intelligence, offering full-stack, contextual analysis powered by deep AI.

Apiiro brings visibility not only to what vulnerabilities exist in code and dependencies, but also to how changes, developer actions, and business context interact to shape risk. Its AI systems process data from source control, CI/CD pipelines, cloud configurations, and user access patterns, allowing it to prioritise remediation based on business impact.

2. Mend.io

Mend.io has rapidly evolved into a cornerstone of the AI-driven AppSec ecosystem, addressing the full spectrum of risks facing software teams today. Using machine learning and advanced analytics, Mend.io is purpose-built to handle the security challenges of code produced by both humans and artificial intelligence.

Leading organisations are attracted to Mend.io’s unified platform, which delivers seamless coverage for source code, open source, containers, and AI-generated functional logic. Its capabilities extend far beyond detection, enabling rapid, automated, and context-rich remediation that saves engineering time and reduces business exposure.

3. Burp Suite

Burp Suite has long been a foundational tool for web application security professionals, but its latest AI-driven evolution makes it essential for defending cutting-edge app landscapes. Today, Burp Suite combines traditional manual penetration testing strengths with sophisticated machine learning, delivering smarter scanning and deeper insight than ever before.

Where legacy DAST (Dynamic Application Security Testing) tools might struggle with modern, dynamic, or API-rich applications, Burp Suite’s AI modules adapt to changes in real time, learning from traffic patterns and user behaviours to uncover anomalies and hard-to-spot vulnerabilities.

4. PentestGPT

PentestGPT represents the future of automated offensive security, using generative AI to simulate the tactics of contemporary adversaries. Unlike pattern-based scanners, PentestGPT can devise new attack paths, generate custom payloads, and think creatively about bypassing controls and protections.

PentestGPT blends autonomous testing with educational support: security analysts, testers, and developers can interact with the platform conversationally, gaining hands-on guidance for complex scenarios and real-world exploit development.

5. Garak

Garak is an emerging leader specialising in security for AI-driven applications, specifically, large language models, generative agents, and their integration into wider software systems. As organisations increasingly embed AI into customer interactions, business logic, and automation, new risks have arisen that traditional AppSec tools simply weren’t built to address.

Garak is designed to probe and harden these AI-infused interfaces, ensuring models respond safely and preventing AI-specific exploits like prompt injections and privacy breaches.

Core features of AI-driven AppSec tools

While not every solution offers the same features, most AI-powered application security tools share several core capabilities:

1. Intelligent vulnerability detection

AI models trained on massive datasets of known exploits can spot coding errors, misconfigurations, and insecure dependencies more accurately than static rule-based tools. They adapt over time, improving detection with each new dataset.

2. Automated remediation guidance

One of the major pain points in AppSec is not just finding vulnerabilities but knowing how to fix them. AI tools can generate remediation advice tailored to the specific context, often offering code suggestions or step-by-step fixes.

3. Continuous monitoring and real-time analysis

Instead of one-time scans, AI-powered tools continuously monitor applications in production. They analyse runtime behaviour, API calls, and data flows to spot anomalies that could indicate an active attack.

4. Risk prioritisation

AI can evaluate the severity of each vulnerability based on exploitability, business impact, and external threat intelligence. The ensures that teams focus on the issues most likely to cause real damage.

5. Integration with DevOps workflows

Modern AppSec tools embed directly into CI/CD pipelines, issue trackers, and developer environments. AI accelerates these processes by automating tasks that previously slowed down builds or required manual oversight.

Building resilient software in an AI world

AI-powered application security is not a single tool, process, or department, it’s the foundation on which resilient, innovative, and trusted software is built. In 2025, the leaders in this space are not just those who scan for vulnerabilities, but those who can learn, adapt, and protect at the velocity of AI-driven innovation.

From comprehensive risk intelligence and agile remediation to the defense of AI-generated code and AI agents themselves, today’s AppSec solutions are reshaping what’s possible, and what’s necessary, for digital security in any industry.

Guest author: Or Hillel, Green Lamp

The post The 5 best AI AppSec tools in 2025 appeared first on AI News.

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.

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The post The value gap from AI investments is widening dangerously fast appeared first on AI News.

Nature, Published online: 01 October 2025; doi:10.1038/d41586-025-03147-9

Critics slammed attempts by Google, Microsoft and Meta to speed up materials discovery. But behind the hype, there is progress.

Background: Home blood pressure (HBP) is an important parameter that guides clinicians in managing hypertension in patients. However, in using these records to manage patients, physicians face challenges, particularly regarding access, integration, and interpretation of the records when making clinical decisions. Clinical decision support systems (CDSSs) have been proposed to address these challenges; however, current literature reveals significant heterogeneity and gaps in CDSSs used for hypertension management. Objective: This study aimed to summarize existing studies on CDSSs that use HBP readings to manage patients with hypertension. Methods: We conducted a scoping review, with searches performed in PubMed, Embase, and Scopus on April 1, 2024. The results were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Studies that used CDSSs integrated with HBP monitoring among adult patients with hypertension in outpatient settings were included. Non-English studies were excluded. Outcomes assessed included the theoretical frameworks used for CDSS development, CDSS components (data capture, processing, and output), clinical outcomes, user experiences, and implementation processes. Results: Of the 5023 articles screened, 33 (0.66%) were included. Most of the studies were conducted in the United States (16/33, 49%) and were randomized controlled trials (21/33, 64%). Nearly two-thirds of the CDSSs (21/33, 64%) were computerized. Only 1 (3%) of the 33 studies reported using a theoretical framework for CDSS development. HBP recording and uploading were predominantly automatic (23/33, 70%). All computerized CDSSs (21/33, 64%) used rule-based algorithms, and most (19/21, 91%) incorporated alert triggers for results outside the reference range. More than a third of the studies (13/33, 39%) were based on hypertension guidelines. Among studies that reported outcomes, most reported improved blood pressure (25/29, 86%) and adjustment in antihypertensive medications (16/19, 84%). Patients and clinicians appreciated the convenience and remote monitoring (10/33, 30%) but reported challenges with usability and access to computerized CDSSs (2/21, 10%). Of the studies using noncomputerized CDSSs (12/33, 36%), all incorporated patient education, while nearly two-thirds of the studies using computerized CDSSs (13/21, 62%) did the same. Clinician training was reported in 5% (1/21) of the computerized CDSSs and 25% (3/12) of the noncomputerized CDSSs. Conclusions: While CDSSs hold promise for improving hypertension management, gaps remain in their development and implementation. Future efforts should focus on integrating robust frameworks; aligning with guidelines; enhancing manual data integration; and addressing usability to maximize effectiveness, adoption, and user satisfaction. Trial Registration: Open Science Framework 26zmn; https://osf.io/26zmn

Nature, Published online: 01 October 2025; doi:10.1038/d41586-025-02954-4

How humans behave.

A team at Microsoft says it used artificial intelligence to discover a “zero day” vulnerability in the biosecurity systems used to prevent the misuse of DNA.

These screening systems are designed to stop people from purchasing genetic sequences that could be used to create deadly toxins or pathogens. But now researchers led by Microsoft’s chief scientist, Eric Horvitz, say they have figured out how to bypass the protections in a way previously unknown to defenders. 

The team described its work today in the journal Science.

Horvitz and his team focused on generative AI algorithms that propose new protein shapes. These types of programs are already fueling the hunt for new drugs at well-funded startups like Generate Biomedicines and Isomorphic Labs, a spinout of Google. 

The problem is that such systems are potentially “dual use.” They can use their training sets to generate both beneficial molecules and harmful ones.

Microsoft says it began a “red-teaming” test of AI’s dual-use potential in 2023 in order to determine whether “adversarial AI protein design” could help bioterrorists manufacture harmful proteins. 

The safeguard that Microsoft attacked is what’s known as biosecurity screening software. To manufacture a protein, researchers typically need to order a corresponding DNA sequence from a commercial vendor, which they can then install in a cell. Those vendors use screening software to compare incoming orders with known toxins or pathogens. A close match will set off an alert.

To design its attack, Microsoft used several generative protein models (including its own, called EvoDiff) to redesign toxins—changing their structure in a way that let them slip past screening software but was predicted to keep their deadly function intact.

The researchers say the exercise was entirely digital and they never produced any toxic proteins. That was to avoid any perception that the company was developing bioweapons. 

Before publishing the results, Microsoft says, it alerted the US government and software makers, who’ve already patched their systems, although some AI-designed molecules can still escape detection. 

“The patch is incomplete, and the state of the art is changing. But this isn’t a one-and-done thing. It’s the start of even more testing,” says Adam Clore, director of technology R&D at Integrated DNA Technologies, a large manufacturer of DNA, who is a coauthor on the Microsoft report. “We’re in something of an arms race.”

To make sure nobody misuses the research, the researchers say, they’re not disclosing some of their code and didn’t reveal what toxic proteins they asked the AI to redesign. However, some dangerous proteins are well known, like ricin—a poison found in castor beans—and the infectious prions that are the cause of mad-cow disease.

“This finding, combined with rapid advances in AI-enabled biological modeling, demonstrates the clear and urgent need for enhanced nucleic acid synthesis screening procedures coupled with a reliable enforcement and verification mechanism,” says Dean Ball, a fellow at the Foundation for American Innovation, a think tank in San Francisco.

Ball notes that the US government already considers screening of DNA orders a key line of security. Last May, in an executive order on biological research safety, President Trump called for an overall revamp of that system, although so far the White House hasn’t released new recommendations.

Others doubt that commercial DNA synthesis is the best point of defense against bad actors. Michael Cohen, an AI-safety researcher at the University of California, Berkeley, believes there will always be ways to disguise sequences and that Microsoft could have made its test harder.

“The challenge appears weak, and their patched tools fail a lot,” says Cohen. “There seems to be an unwillingness to admit that sometime soon, we’re going to have to retreat from this supposed choke point, so we should start looking around for ground that we can actually hold.” 

Cohen says biosecurity should probably be built into the AI systems themselves—either directly or via controls over what information they give. 

But Clore says monitoring gene synthesis is still a practical approach to detecting biothreats, since the manufacture of DNA in the US is dominated by a few companies that work closely with the government. By contrast, the technology used to build and train AI models is more widespread. “You can’t put that genie back in the bottle,” says Clore. “If you have the resources to try to trick us into making a DNA sequence, you can probably train a large language model.”

Nature, Published online: 01 October 2025; doi:10.1038/d41586-025-03094-5

The country’s output in quality health research is going from strength to strength, but can it overcome questions about the integrity of its publishing practices?

Background: Development and use of digital public health tools surged during the COVID-19 pandemic. Among these tools, vaccine verification systems emerged as alternatives to paper vaccine records, aiming to help limit the spread of disease. In November 2021, the Washington State Department of Health launched “WA Verify,” a QR code–based vaccine verification system built on the SMART Health Card framework, providing residents with a convenient way to store and share proof of vaccination digitally. However, WA Verify was developed and deployed before assessments and public input regarding potential adoption challenges—such as concerns about privacy, surveillance, data sharing, trust in the technology, and the managing organizations—could be completed. Objective: This analysis used statewide survey data from Washington to identify and characterize barriers and facilitators to the adoption of WA Verify, and to understand how factors such as data privacy, security, attitudes toward public health policies and communication, and technological proficiency may influence acceptance and uptake of digital public health tools. Methods: A cross-sectional statewide survey was distributed between September 2022 and January 2023 to a random sample of 5000 Washington households. Respondents were categorized into 3 groups based on their responses indicating WA Verify “users,” “potential users,” or “unlikely users.” Comparisons were made between groups regarding experiences with and opinions on COVID-19 vaccine and test verification, public health policies, communication, digital tools, technological proficiency, sociodemographic characteristics, and health history. Poststratification weights were applied to reduce nonresponse bias. Results: Of the 1401 respondents, 359 (25.6% unweighted, 25.8% weighted) were users, 662 (47.3% unweighted, 49.8% weighted) were potential users, and 380 (27.1% unweighted, 24.4% weighted) were unlikely users. All percentages reported are based on weighted data. Compared with users and potential users, unlikely users were more likely to oppose policies requiring proof of COVID-19 vaccination or negative test results (users: 6.0%, potential users: 13.6%, unlikely users: 65.9%). Unlikely users were more likely to cite concerns about personal health data security and phone hacking or tracking, though these concerns were also notable among potential users and users. Users and potential users were more likely to perceive a digital vaccine verification system as convenient (users: 96.5%, potential users: 92.3%, unlikely users: 38.1%) and indicated openness to receiving relevant information from a range of sources. Unlikely users were more likely to report not owning a smartphone and demonstrated lower technological proficiency (users: 12.3%, potential users: 15.9%, unlikely users: 32.3%), indicating a technological divide between groups. Conclusions: While nearly three-quarters of respondents had either already adopted or were willing to adopt a tool like WA Verify, concerns about data security, lower technological proficiency, and distrust of public health characterized those least likely to adopt such tools. Identifying barriers to adoption among “unlikely users” is essential for developing effective communication strategies—such as targeted marketing and community engagement—to improve adoption and ensure equitable access to public health technologies.

Talk of AI is inescapable. It’s often the main topic of discussion at board and executive meetings, at corporate retreats, and in the media. A record 58% of S&P 500 companies mentioned AI in their second-quarter earnings calls, according to Goldman Sachs.

But it’s difficult to walk the talk. Just 5% of generative AI pilots are driving measurable profit-and-loss impact, according to a recent MIT study. That means 95% of generative AI pilots are realizing zero return, despite significant attention and investment.

Although we’re nearly three years past the watershed moment of ChatGPT’s public release, the vast majority of organizations are stalling out in AI. Something is broken. What is it?

Date from Lucid’s AI readiness survey sheds some light on the tripwires that are making organizations stumble. Fortunately, solving these problems doesn’t require recruiting top AI talent worth hundreds of millions of dollars, at least for most companies. Instead, as they race to implement AI quickly and successfully, leaders need to bring greater rigor and structure to their operational processes.

Operations are the gap between AI’s promise and practical adoption

I can’t fault any leader for moving as fast as possible with their implementation of AI. In many cases, the existential survival of their company—and their own employment—depends on it. The promised benefits to improve productivity, reduce costs, and enhance communication are transformational, which is why speed is paramount.

But while moving quickly, leaders are skipping foundational steps required for any technology implementation to be successful. Our survey research found that more than 60% of knowledge workers believe their organization’s AI strategy is only somewhat to not at all well aligned with operational capabilities.

AI can process unstructured data, but AI will only create more headaches for unstructured organizations. As Bill Gates said, “The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.”

Where are the operations gaps in AI implementations? Our survey found that approximately half of respondents (49%) cite undocumented or ad-hoc processes impacting efficiency sometimes; 22% say this happens often or always.

The primary challenge of AI transformation lies not in the technology itself, but in the final step of integrating it into daily workflows. We can compare this to the “last mile problem” in logistics: The most difficult part of a delivery is getting the product to the customer, no matter how efficient the rest of the process is.

In AI, the “last mile” is the crucial task of embedding AI into real-world business operations. Organizations have access to powerful models but struggle to connect them to the people who need to use them. The power of AI is wasted if it’s not effectively integrated into business operations, and that requires clear documentation of those operations.

Capturing, documenting, and distributing knowledge at scale is critical to organizational success with AI. Yet our survey showed only 16% of respondents say their workflows are extremely well-documented. The top barriers to proper documentation are a lack of time, cited by 40% of respondents, and a lack of tools, cited by 30%.

The challenge of integrating new technology with old processes was perfectly illustrated in a recent meeting I had with a Fortune 500 executive. The company is pushing for significant productivity gains with AI, but it still relies on an outdated collaboration tool that was never designed for teamwork. This situation highlights the very challenge our survey uncovered: Powerful AI initiatives can stall if teams lack modern collaboration and documentation tools.

This disconnect shows that AI adoption is about more than just the technology itself. For it to truly succeed enterprise-wide, companies need to provide a unified space for teams to brainstorm, plan, document, and make decisions. The fundamentals of successful technology adoption still hold true: You need the right tools to enable collaboration and documentation for AI to truly make an impact.

Collaboration and change management are hidden blockers to AI implementation

A company’s approach to AI is perceived very differently depending on an employee’s role. While 61% of C-suite executives believe their company’s strategy is well-considered, that number drops to 49% for managers and just 36% for entry-level employees, as our survey found.

Just like with product development, building a successful AI strategy requires a structured approach. Leaders and teams need a collaborative space to come together, brainstorm, prioritize the most promising opportunities, and map out a clear path forward. As many companies have embraced hybrid or distributed work, supporting remote collaboration with digital tools becomes even more important.

We recently used AI to streamline a strategic challenge for our executive team. A product leader used it to generate a comprehensive preparatory memo in a fraction of the typical time, complete with summaries, benchmarks, and recommendations.

Despite this efficiency, the AI-generated document was merely the foundation. We still had to meet to debate the specifics, prioritize actions, assign ownership, and formally document our decisions and next steps.

According to our survey, 23% of respondents reported that collaboration is frequently a bottleneck in complex work. Employees are willing to embrace change, but friction from poor collaboration adds risk and reduces the potential impact of AI.

Operational readiness enhances your AI readiness

Operations lacking structure are preventing many organizations from implementing AI successfully. We asked teams about their top needs to help them adapt to AI. At the top of their lists were document collaboration (cited by 37% of respondents), process documentation (34%), and visual workflows (33%).

Notice that none of these requests are for more sophisticated AI. The technology is plenty capable already, and most organizations are still just scratching the surface of its full potential. Instead, what teams want most is ensuring the fundamentals around processes, documentation, and collaboration are covered.

AI offers a significant opportunity for organizations to gain a competitive edge in productivity and efficiency. But moving fast isn’t a guarantee of success. The companies best positioned for successful AI adoption are those that invest in operational excellence, down to the last mile.

This content was produced by Lucid Software. It was not written by MIT Technology Review’s editorial staff.

Nature Biotechnology, Published online: 01 October 2025; doi:10.1038/s41587-025-02844-0

A microchip-based DNA synthesis method enables scalable production of complex DNA constructs.