Category: 3. Business

Introduction

Atherosclerotic cardiovascular disease (ASCVD) remains one of the top global health concerns []. It is estimated that 80% of ASCVD cases are preventable []. Strategies to identify and target people at higher risk are therefore crucial to reducing the global disease burden, especially in resource-constrained settings with limited access to medical specialists and advanced equipment [,].

Primary care doctors play an important role in preventing ASCVD by assessing and managing cardiovascular risk factors. Risk assessment is centered on routine screening of asymptomatic adults 40‐75 years of age to determine the risk of heart disease or stroke in the next 10 years, allowing for more focused primary preventive interventions. The intensity of interventions must align with the patient’s individual risk, optimizing anticipated benefits while minimizing potential harm from overtreatment and allocating limited resources appropriately []. Doctors often use clinical decision support (CDS) tools to calculate ASCVD risk and guide shared decision-making for preventive management [,]. In resource-constrained settings however, medical records are predominantly maintained on paper, and doctors use CDS on mobile devices [] (AP Susanto, PhD, et al; unpublished data, August 2024). This situation warrants an automated process to enter patient data into the CDS, as manual data entry is prone to errors, especially under high patient loads (AP Susanto, PhD, et al; unpublished data, August 2024).

Recently, CDSs are being embedded with artificial intelligence (AI) [], specifically machine learning (ML) models that can accommodate patient-level precision beyond traditional risk factors. These AI-based CDSs provide new opportunities to improve risk assessment in poorly served patient subgroups and consider nontraditional risk factors, such as psychosocial factors, or inflammation [,]. While ML models have demonstrated superior performance compared to statistics-based ASCVD risk calculators [], their impact on enhancing clinical decisions, which is an essential precursor for realizing benefits in care delivery and patient outcomes, remains unknown []. In a recent review of AI tools in routine care, only 7 studies examined clinical decision-making, none of the 86 randomized controlled trials published in the last 6 years focused on risk assessment for cardiovascular disease (CVD), and only 4 were conducted in resource-constrained settings [].

We sought to evaluate whether AI-based CDS can improve clinical decisions for the primary prevention of ASCVD in Indonesia, where CVD accounts for the highest health care expenditure []. The high prevalence of CVD risk in Indonesia (29.2%) underscores the need to prioritize preventive services []. Accurate risk assessment is fundamental to preventive care, yet it remains a persistent challenge, especially among high-risk rural residents who are less likely to receive such care. Consequently, many individuals at elevated risk are not identified, whereas others without risk are unnecessarily subjected to preventive interventions. A randomized controlled study was conducted to assess the effect of AI-based CDS on 10-year ASCVD risk assessment and management, using clinical vignettes with a conceptual prototype of a CDS. Clinical vignettes or patient cases offer greater study control to quantify the effects of AI-based CDS on decision-making before costly and potentially disruptive clinical deployment, allowing for evaluation without putting real patients or doctors at risk [,]. The patient cases were based on our previous work, which examined the sociotechnical context of CVD risk assessment in resource-constrained settings with limited access to medical specialists and advanced equipment (AP Susanto, PhD, et al; unpublished data, August 2024). Acute coronary syndrome caused by atherosclerosis was confirmed as the most common CVD challenge, requiring primary prevention to alleviate the disease burden. In this study, we compared AI-based CDS against the current approach to risk assessment with either no CDS or automated CDS, whereby patient data were automatically populated into the CDS. The technology acceptance of CDS was also examined.

Methods

Setting and Participants

In total, 102 doctors participated in the online study between November and December 2023. Participants were recruited by publishing a call for volunteers via the Indonesian Heart Association at 5 training centers located in different geographical regions. Doctors who were working in general practice or enrolled in a cardiology training program were eligible to participate. The study invitation was also distributed to potential participants via email and WhatsApp (Meta Platforms).

Design

We performed a 3-way, within-subject randomized controlled study to evaluate the effect of CDS type: standard care with no CDS, automated CDS, and AI-based CDS (see and the Intervention section for details). To simulate variation in the clinical context, each CDS type was provided with 3 patient cases with different case complexity levels: low, high, and high with risk-enhancing factors (REFs) based on the literature [,]. This design provided 9 patient cases related to primary prevention in an outpatient setting for adults aged 40-75 years without a history of ASCVD (AP Susanto, PhD, et al; unpublished data, August 2024). Low-complexity cases only contained information about the traditional risk factors, including age, sex, race, blood pressure, cholesterol level, history of diabetes, smoking status, and previous medications including antihypertensives, statin, and aspirin. High-complexity cases included the presence (3 cases of high complexity with REFs) or absence (3 high-complexity cases) of REFs such as family history of premature heart attack and obesity, on top of the traditional risk factors.

The study was deployed using Gorilla.sc, a platform for conducting online studies, which was customized to display patient cases and CDS interventions []. The allocation of cases to the different CDS types and the presentation order were randomized so that each case would appear evenly across the 3 CDS types. All randomizations were set equal (1:1) and performed by the Gorilla software (v2; Cauldron Science).

Intervention

The CDS type provided to participants was manipulated across 3 conditions as follows:

1. No decision support, that is, the current approach to risk assessment served as the control condition. Only the information present in the case was shown (see ).
2. Automated CDS: This was designed to augment the current approach to risk assessment whereby patient data were automatically populated, and the CDS output was displayed to participants, streamlining screening for doctors with high patient load. The automated CDS presented the 10-year ASCVD risk level, risk score (in %), and treatment advice in English. It was based on the publicly available ASCVD Risk Estimator Plus calculator from the American College of Cardiology [], which is widely accepted in Indonesia. Risk is calculated using Pooled Cohort Equations, a conventional statistical method based on traditional risk factors. Although the display of the original calculator was not altered, the logo was concealed (see ).
3. AI-based CDS: This was designed to represent contemporary risk calculators based on traditional ML models (eg, random forest, neural networks, logistic regression) that account for additional risk factors on top of the traditional risk factors and have been shown to perform better in risk discrimination compared to traditional risk estimators []. Here, a conceptual prototype of an AI-based CDS was constructed similar to previous studies examining doctors’ perceptions about ML-based risk calculators []. The AI-based CDS presented the 10-year ASCVD risk level and treatment in English but did not provide a risk score (in %). The CDS output, that is, the risk assessment and treatment advice for each case, was constructed by a general practitioner (GP; APS), then scored independently by 2 consultant cardiologists (DAJ and AS), and reviewed by a third consultant cardiologist (BW) based on their clinical experience in managing patients with CVD in Indonesia. All disagreements were resolved by consensus in a workshop (see and ).

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Blinding was implemented to minimize placebo effects. The allocation of patient cases to CDS type was according to 9 pregenerated sequences, created so that cases were allocated evenly across each of the 3 conditions. Participants were allocated to sequences at the time of enrollment using balanced randomization, with sequences presented in random order. All conditions were displayed using an identical user interface. While participants were able to distinguish between the control (no decision support) and intervention conditions, they were blinded to the intervention conditions. The automated CDS and AI-based CDS were generically coded as “Risk Calculator 1” and “Risk Calculator 2,” respectively, with an identical presentation (). We did not specify which tool harnessed AI to minimize placebo effects that could influence doctors’ performance []. In all conditions, participants could access 2 clinical guidelines on ASCVD prevention [,], commonly referred to by Indonesian doctors [].

Patient Cases

The patient cases were based on the clinical guidelines for the primary prevention of CVD [,], covering typical presentations of asymptomatic adults 40‐75 years of age in an outpatient setting. Participants were presented with 9 patient cases and asked to assess the 10-year risk of ASCVD and provide the most appropriate patient management. As shown in , each case included a brief patient history, physical examination, and simple laboratory results commonly available in resource-constrained clinical settings (). The study setup and the CDS interface were designed to emulate access on a mobile device, which is a likely real-world implementation of such tools in resource-constrained settings [].

Cases and questions were presented in Bahasa Indonesia to ensure clarity. The cases were developed by a GP (APS) with advice from a senior cardiologist consultant (BW). Risk factors, risk assessment, and patient management in each case were coded by APS and reviewed by BW; disagreements were resolved by consensus. The gold standard for risk assessment was determined based on the clinical guidelines, and cases with REFs were reclassified based on clinical relevance. Cases and gold standard responses for correct risk assessment and patient management were independently reviewed by an expert panel of 2 senior cardiology consultants (DAJ and AS) to ensure clinical relevance. Disagreements were resolved by consensus via a finalization workshop.

Procedure

Participants self-enrolled in the online study. The study URL was provided in the invitation, and participants were asked to access the study from a laptop or desktop computer with an internet connection at their convenience. After informed consent, participants provided demographic information and watched an instructional video (2 min) explaining the study task and CDS interventions, orienting participants with the study user interface.

Participants were also shown how to view clinical guidelines that were accessible online. At the end of the video, participants had the opportunity to explore the user interface and CDS interventions with a demonstration case and could repeat the demonstration case once.

Participants were randomly allocated a pregenerated sequence of 9 cases. Cases that were allocated to the automated CDS (blinded as “Risk Calculator 1”) and AI-based CDS (“Risk Calculator 2”) conditions presented the risk level and patient management. Control cases displayed “No Decision Support” where CDS would not be displayed in the mobile device interface.

Participants were instructed to complete all 9 cases to the best of their clinical judgment in a single, uninterrupted session, and no time limits were imposed. After completing the cases, participants responded to a poststudy survey based on the technology acceptance model (TAM) []. They were then provided with a debriefing note and thanked for their participation. The procedure was pilot-tested by 2 doctors and refined based on their feedback.

Outcome Measures

To assess the effects of AI-based CDS on risk assessment and patient management, the following outcome measures were examined.

Risk Assessment

The response was multiple choice and automatically scored as correct or incorrect against the validated gold standard response for the cases. Given the case, participants determined the 10-year ASCVD risk assessment, defined as a chance of having major CVD events (eg, heart disease or stroke) in the next 10 years. There were 4 levels of risk: low risk (<5%), borderline risk (≥5% to <7.5%), intermediate risk (≥7.5% to <20%), and high risk (≥20%). We counted the number of cases correctly assessed.

Patient Management

As with the risk assessment, the response for patient management was multiple choice and automatically scored as correct or incorrect against the validated gold standard response for the case. Given the case and risk classification, there were 4 measures of patient management for primary prevention: whether the doctor discussed the risk level or score and prescribed aspirin, statins, antihypertensives, and referred patients for advanced examinations. We counted the number of cases correctly managed as follows.

1. Aspirin prescription: Participants chose between not prescribing or prescribing 80‐100 mg of aspirin.
2. Statin prescription: Participants choose 1 of 4 options: (1) no statin therapy, (2) low-intensity statin (eg, simvastatin 10‐20 mg), (3) moderate statin intensity (eg, simvastatin 20‐40 mg or atorvastatin 10‐20 mg), or (4) high-intensity statin (eg, atorvastatin 40‐80 mg or rosuvastatin 20‐40 mg).
3. Antihypertensive prescription: Participants chose between discussing and prescribing antihypertensive medications or not.
4. Advanced examination referral: Participants chose between referring and not referring patients to a facility located 1‐3 hours away for advanced examinations such as treadmill stress test [], computed tomography calcium score [], or computed tomography coronary angiography [].

Decision-Making Time

Decision-making time was automatically measured from when the case was first presented until the responses for risk assessment and diagnosis were submitted.

Technology Acceptance

Participant perceptions about the potential utility of AI for ASCVD risk assessment in resource-constrained settings were examined using mixed methods []. In the quantitative component, we examined perceptions about the automated CDS and AI-based CDS using the TAM, including the usefulness (2 items), ease of use (1), and behavioral intention of CDS usage (2) using a 7-point Likert scale ranging from (“strongly disagree”) to 7 (“strongly agree”). Participants’ free-text responses to “comments about CDS” were coded into different factors of the TAM by APS and reviewed by FM.

We also measured effects of case complexity, diagnosis, choice of specific antihypertensive regimen, and justification for referrals; these analyses will be reported separately.

Statistical Analysis

We estimated that 61 participants would be required to detect a 39% or greater difference (2-tailed) in decisions with and without CDS with 90% power and at P<.05 []. All measures for risk assessment and patient management were non-normally distributed, and therefore, the effects of CDS were tested by using the Friedman test to compare multiple repeated measures of cases correctly assessed and managed across CDS types. Subsequent CDS pairwise comparisons with Bonferroni-adjusted P values and effect sizes were performed between the CDS type pair.

Decision-making time was analyzed by a multilevel model with outliers removed (see ) [,]. The model included a random intercept for each participant, controlling the nested nature of data. Predictors assessed for inclusion in the model were CDS type and case complexity. A stepwise backward elimination method was used for predictor selection, where all predictors and interactions were entered into the model. Models were estimated using maximum likelihood, and fit was evaluated. Predictors that significantly improved model fit were retained.

For technology acceptance, the median of all participant scores was calculated for each item. To assess the internal consistency of the TAM factors in the questionnaire, we calculated the mean of the items for each factor and Cronbach α [].

All statistical analyses were undertaken using the SPSS software (v27; IBM Corp). Reporting was guided by the health care simulation research extensions of the CONSORT (Consolidated Standards of Reporting Trials) and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statements (see ) [].

Ethical Considerations

This study was approved by the Macquarie University Human Research Ethics Committee (reference: 520221032837485; project: 10328) and Faculty of Medicine Universitas Indonesia Ethics Committee (KET-586/UN2.F1/ETIK/PPM.00.02/2022). Participant consent was obtained via an online participant information sheet and consent form in accordance with approved protocols. Participants were offered an IDR 500,000 (US $32) e-commerce voucher for their contribution. No personally identifying information was used for analysis and reporting. Patients presented in the cases were hypothetical, biographical information was generated solely for the purposes of this study, and patient photos were produced by generative AI (Dall-E by OpenAI in October 2023). This study was not prospectively registered, as it was conducted using a conceptual prototype with clinical vignettes rather than real patients. It was retrospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12625001168448 []).

Results

Demographic Characteristics

In total, 102 Indonesian doctors participated in the online study (; ). The participants were distributed across all 7 geographical units of the Indonesian archipelago (). Most (n=57, 56%) identified as male and were aged 26‐35 years (n=85, 83%), with a median of 6 (IQR 4.75) years of clinical experience. Of these, 63 (62%) were enrolled in a 4-year graduate cardiology residency training program, which requires 1‐5 years of experience as a GP following graduation from undergraduate medical school and is usually undertaken in a remote area.

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AI-Based CDS Improved Risk Assessment

We examined the effect of CDS type on risk assessment by comparing cases correctly assessed across the 3 study conditions: standard care with no CDS (control), automated CDS, and AI-based CDS (). The analysis revealed a statistically significant difference in risk assessment by CDS type (: P<.001). In pairwise comparisons, a significantly higher number of cases were correctly assessed when doctors were assisted by the AI-based CDS compared to the control (z=−5.602, n=102, adjusted P<.001, medium effect size r=0.39) and the automated CDS (z=−4.901, n=102, adjusted P<.001, medium effect size r=0.34). However, the improvement in risk assessment with the automated CDS compared to the control was nonsignificant (z=−0.700, n=102, adjusted P>.99).

Effects of AI-Based CDS on Patient Management Were Mixed

We observed a mixed effect of CDS type on the 4 patient management measures, including the prescription of aspirin, statin, antihypertensive medications, and referral for advanced examinations ( and ). While CDS type had a statistically significant effect on the prescription of statins (P<.001) and referrals (P=.03), its effect on the prescription of aspirin and antihypertensives was nonsignificant.

In pairwise comparisons, a significantly higher number of cases were prescribed with the appropriate statin intensity when doctors were assisted by the AI-based CDS (z=−4.936, adjusted P<.001, medium effect size r=0.35) and the automated CDS (z=−3.991, adjusted P<.001, small effect size r=0.28). For referrals, no differences were revealed in the pairwise comparison. While fewer cases were correctly referred with AI-based CDS compared to the control (z=0.230, adjusted P=.30) and automated CDS (z=2.135, adjusted P=.30), these differences were nonsignificant.

AI-Based CDS Decreased Decision-Making Time

Cases assisted by the AI-based CDS (P=.02) and automated CDS (P=.006) took less time than the control (see for estimated marginal means). There was no significant difference in the decision-making time between cases assisted by the automated CDS and AI-based CDS.

Perceptions About CDS

We measured doctors’ perceptions about automated CDS and AI-based CDS using the TAM. Overall, doctors agreed with statements measuring the perceived ease of use, usefulness, and behavioral intention to use CDS, with a median score of 6 for all items (ie, “agree”). The value for Cronbach α for the survey was ≥.7, showing internal consistency within the factors. Of the 102 respondents, 81 (79%) agreed or strongly agreed to using recommendations from AI-based CDS and 82 (82%) agreed or strongly agreed to use it if given access. Perceptions about the accuracy of the AI-based CDS were largely positive, where 76 (76%) participants perceived their decisions on risk more accurate, while 6 (6%) somewhat disagreed with its accuracy.

For the automated CDS, 72 (70%) agreed or strongly agreed to using its recommendations, and 85 (83%) agreed or strongly agreed to using it if given access. Perceptions about the accuracy of the automated CDS were largely positive, where 75 (73%) participants perceived their decisions on risk were more accurate, while 3 (3%) somewhat disagreed with its accuracy ().

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Qualitative comments highlighted the overall usefulness of both CDS in providing comprehensive patient management (). Doctors believed that CDS had the potential to improve the efficiency of risk assessment and decision-making, particularly in primary care settings where patient volumes are high. While the AI-based CDS was perceived to be more accurate, the need to verify recommendations against clinical guidelines based on the patient’s actual condition was noted. The necessity for the AI-based CDS to offer explanations of its risk scores was also highlighted (, Doctor 014, Doctor 007). Automated input of patient data and the ability to work offline without an internet connection were identified as essential features for ease of use.

Discussion

Principal Results and Implications

We found that use of AI-based CDS significantly improved 10-year ASCVD risk assessment and decreased decision-making time, with mixed effects on patient management. Overall, participants’ perceptions about AI assistance were favorable. The observed improvement indicates potential for AI-based CDS to improve routine screening for CVD risk factors in resource-constrained settings. The number needed to treat for benefit associated with AI-based CDS was around 4, indicating that for every 4 patients where doctors use AI-based CDS as the “treatment,” there will be 1 additional correct risk classification as the “outcome” (number needed to treat=3.7, 95% CI 2.9-5.2). Assuming daily primary care visits of 30-100 patients per doctor (AP Susanto, PhD, et al; unpublished data, August 2024), with 12% requiring ASCVD risk assessment [], this amounts to an additional 1-3 accurate risk assessments per day, totaling 250-750 per year. Accordingly, these findings suggest the need for further research in a live clinical environment to determine whether improvements observed in the online study can be translated into real-world clinical settings.

For AI-based CDS to provide value, the decisions made by doctors must trigger some action or the CDS must provide actionable recommendations []. We measured patient management as an indicator of change in actionable care delivery, specifically the correct prescription of primary preventive medications (aspirin and statin), prescription of antihypertensive medications, and referral for advanced examinations. The findings indicated mixed effects on care delivery. Most importantly, AI-based CDS positively impacted the prescription of statins, showing that primary prevention of ASCVD could be improved by deploying AI tools to assist doctors. This is consistent with previous studies in Indonesia that identified gaps in awareness about the prescription of statins [], which, when prescribed at an appropriate intensity based on individual risk assessments, can reduce the 10-year incidence of ASCVD, achieve lipid target levels, and minimize medication side effects for long-term safety [].

Conversely, the effects of AI-based CDS in improving the prescription of aspirin and antihypertensive medications did not reach statistical significance. One possible explanation is that doctors may consider blood pressure independently of the ASCVD risk assessment and CDS recommendation when prescribing antihypertensives []. For aspirin, which had fewer cases of correct prescription, doctors appeared to ignore the CDS recommendations or did not see the guideline. Another possibility is a ceiling effect: the baseline correctness for these 3 measures in the control group (67.3% and 80.1%, respectively) was already substantially higher than for statins (35.3%), leaving less room for improvement. Consequently, doctors may have benefited less from the CDS when prescribing aspirin and antihypertensives, thereby limiting potential gains. Further research is needed to understand these effects.

We could not ascertain why AI-based CDS did not improve referral for advanced examinations. One possible explanation is that referrals were not included in the AI-based CDS recommendations, requiring doctors to infer the need for referral based on their knowledge of local access and clinical judgment. This is consistent with the ambiguity in the current national guidelines, which are silent about the referral of high-risk patients for advanced examination, possibly due to variations in local resources and policies. As such, this finding implies that the use of AI-based CDS, particularly for risk assessment, should be augmented with clear clinical consensus and local policy to enhance the actionability of recommendations to avoid mixed or negative effects on care delivery.

Delivering Fast and Acceptable AI-Based CDS

We found that use of AI-based CDS reduced decision-making time by 12% compared to the control group, even though doctors with AI assistance received additional information and confirmed the risk assessment provided by the system. While the reduction in decision-making time is statistically significant, the 10-second difference may not be clinically meaningful. Nevertheless, this finding implied that decision-making assisted by AI remained faster than heuristic risk assessment without CDS. This suggests that using AI-based CDS decreases decision time, despite requiring doctors to review and confirm the AI output.

The observed effects on decision-making time are consistent with previous studies of AI in primary care. For instance, a randomized controlled study of cataract screening in a remote area suggested faster diagnosis when supported by AI-based CDS compared to doctors alone (2.79 min vs 8.52 min, P<.001) []. In another study simulating AI-based CDS for the treatment of depression, 40% of doctors indicated that using CDS would save time []. Thus, AI-based CDS may be especially valuable in high-volume, resource-constrained settings where clinician time is scarce (AP Susanto, PhD, et al; unpublished data, August 2024).

Doctors generally viewed CDS for ASCVD risk assessment positively, but they raised concerns about its accuracy, guideline adherence, and capacity to address patients comprehensively. Similar concerns were observed with the use of AI-based risk assessment for delirium []. In our study, most doctors found AI-based CDS useful for decision-making, and their intention to use it was reinforced by perceptions of its high accuracy. Conversely, an AI system for sepsis prediction was poorly rated due to late, less useful alerts and a lack of transparency in its “black-box” results []. Doctors who perceive AI as beneficial are more likely to adopt it in clinical practice.

Safe Use of AI-Based CDS

Despite having access to highly accurate AI-based CDS, approximately 89 (29%) of patient cases were inaccurately assessed, as doctors failed to recognize accurate recommendations. This proportion appears to compare with 24 (24%) participants, who were neutral or somewhat disagreed with the accuracy of the AI in the TAM survey. Participant comments suggested that doctors sought explanations when their risk estimates diverged from CDS recommendations (). In such cases, doctors might have relied on their own risk estimates due to the AI’s lack of explanation.

For future development and validation, one way to increase doctors’ confidence and performance is for AI-based CDS to provide an explanation as demonstrated by the mock-up in . Explanations could be model-agnostic and local, displaying risk factors, input variables, their marginal contributions, and risk scores, rather than only providing on risk level recommendations. Previous studies demonstrated that model-agnostic local explanations, which include individual risk factors, can help doctors comprehend, trust, and explain outputs to patients []. As doctors prefer simple visualizations and relevant clinical reasoning, AI should empower them to offer meaningful explanations to patients, facilitating shared decision-making.

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Our results suggest that doctors need support through relevant clinical guidelines and training to make informed decisions about using and evaluating AI recommendations. Although participants had access to clinical guidelines, and comments emphasized their importance (), our findings are consistent with a previous study of prescribing decision support with 102 participants, where less accurate recommendations were accepted despite the availability of guidelines for verification []. Differently from high-resource settings, where verification can involve advanced examinations or medical specialists, in resource-constrained settings, verification mechanisms may be limited to simple tools such as guidelines. Here, doctors must proceed with the final decision and treatment assisted by AI-based risk assessment alone. Preemptive training is crucial for safe CDS use, especially with AI, as each tool will operate differently. Doctors need to understand what inputs the AI considers or excludes and be aware of how incorrect use could harm patients []. This study therefore highlights the need for AI developers and users to address safety considerations in AI-based CDS for resource-limited settings, to enhance global health equity rather than introduce new risks or burdens.

Strengths and Limitations

This is the first randomized controlled study to evaluate the effects of AI on ASCVD risk assessment and patient management for primary prevention in a resource-constrained setting. Participants were instructed to perform the risk assessment as they would in a real outpatient setting, exercising their best clinical judgment. The awareness of completing clinical vignettes might have influenced doctors to perform at an enhanced level compared to real-world practice and overestimate improvement []. Conversely, doctors could assume that any inaccurate decision carried no accountable consequence.

We specifically designed the cases for resource-constrained clinical settings where there is access to information on traditional risk factors but limited access to advanced examinations such as computed tomography calcium scores, lipoprotein (a), and medical specialists. Our findings can therefore be generalized to similar settings. However, the spectrum within resource-constrained settings varies both horizontally across different dimensions and vertically within a single dimension []. For example, within the dimension of limited access to advanced examinations, even traditional risk factors like high-density lipoprotein cholesterol tests may be unavailable in some settings. Efforts were made to ensure that the cases were representative of the clinical context; however, this may not reflect the true rate of CVD risk in the population. Although the minimum sample size was determined a priori and was adequate, recruitment through the Indonesian Heart Association at 5 training centers may have led to a participant group more predisposed to research involvement than the broader medical community. Therefore, the findings may not be generalizable to the broader population of Indonesian GPs, who may be older, have different training backgrounds, and practice in more isolated community health centers ().

Additionally, we used a conceptual prototype of an AI-based CDS that accounted for REFs based on expert consensus, which may not reflect the true performance difference between an actual AI-based tool and the existing risk calculator. Actual AI-based tools have dynamic, rather than fixed, performance as they are continually trained on new data. In addition, the conceptual prototype did not account for practical challenges such as input data quality and model transportability across settings, which may limit the generalizability of the results to real-world settings. Nevertheless, this study has demonstrated the potential of AI-based CDS to improve risk assessment by accounting for patient-level precision beyond traditional risk factors and sets the foundation for further studies to test AI-based tools ahead of clinical implementation.

Conclusions

Improvements in ASCVD risk assessment with a conceptual prototype demonstrate that use of AI can support better and faster decision-making in resource-constrained settings and are moderated by doctors’ positive perceptions.

SANTA CLARA, Calif., Nov. 25, 2025 – Intel Corporation today announced that John Pitzer, corporate vice president, Global Treasury and Investor Relations, will participate in fireside chats to discuss Intel’s business and strategy at the following investor events:

• On Dec. 3 at 1:15 p.m. PT: UBS Global Technology and AI Conference.
• On Dec. 10 at 11:35 a.m. PT: Barclays Global Technology Conference. 

Live webcasts and replays can be accessed publicly on Intel’s Investor Relations website at intc.com.

Intel’s participation, speakers and schedule are subject to change.

Note: This story has been updated to reflect a revised date and time for the upcoming UBS Global Technology and AI Conference fireside chat.

About Intel

Intel (Nasdaq: INTC) designs and manufactures advanced semiconductors that connect and power the modern world. Every day, our engineers create new technologies that enhance and shape the future of computing to enable new possibilities for every customer we serve. Learn more at intel.com. 

© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

Contacts:

Investor Relations
investor.relations@intel.com

Sophie Metzger
Media Relations
sophie.metzger@intel.com

Released Nov 25, 2025 • 4:25 PM EST

Good morning and welcome back to FirstFT Asia. In today’s newsletter:

Volkswagen has said it can produce an electric vehicle entirely made in China for half the cost of doing so elsewhere, as the German automaker fights to reclaim its share in the world’s biggest market.

What to know: Europe’s largest carmaker said yesterday that, following a series of investments in China, it can for the first time develop new cars outside Germany. VW is preparing to release about 30 EV models in China over the next five years, in a bet on localised research and development. The carmaker said that, compared with the 2023 production costs for EVs in Germany, the cost for some models in China had been reduced by as much as 50 per cent because of supply chain efficiencies.

VW was for many years the biggest carmaker in China but it has lost market share as a wave of innovative Chinese EV rivals won over consumers.

VW’s China plans: The group is in discussions about increasing exports of Chinese-made cars as well as applying China breakthroughs throughout its global operations, said executives. VW is among a clutch of European carmakers attempting to replicate the speed of vehicle development in China by hiring local engineers, collaborating with partners there and by tearing down vehicles made by BYD and other newer rivals. Read more about VW’s efforts to survive in China.

Here’s what else we’re keeping tabs on today:

• Economic data: Australia publishes October inflation data.

• UK economy: Chancellor Rachel Reeves unveils a massive tax-raising Budget in what is perhaps the defining event of this parliament.

Join senior Financial Times editors on December 3 for a discussion on what will shape the year ahead. Register for free today.

Five more top stories

1. Donald Trump said he would dispatch special envoy Steve Witkoff to meet Russian leader Vladimir Putin in Moscow in another push to secure a peace plan to end the Kremlin’s war in Ukraine. The US president said his negotiators had made “tremendous progress” in ending the war — but he would wait until a deal was within reach before meeting Ukraine’s leader Volodymyr Zelenskyy and Putin. Here’s the latest on the peace talks.

2. Samsung chair Lee Jae-yong hosted Mukesh Ambani, Asia’s richest man and chair of India’s Reliance Industries, for talks over deepening the relationship between two of the region’s most powerful conglomerates. Lee has been holding a series of high-profile meetings as he seeks to keep his company in a strong position for the global AI build-out.

3. Nvidia shares fell sharply yesterday on fears that Google is gaining ground in AI, erasing $150bn in market value from the chipmaker. Google last week released Gemini 3, its latest large language model, which is considered to have leapfrogged OpenAI’s ChatGPT. Analysts likened Gemini 3’s impact to the disruption triggered by Chinese start-up DeepSeek.

4. Indonesia is investigating radioactive contamination at an industrial zone after US and Dutch authorities found unusual levels of radiation in some of the south-east Asian country’s biggest exports. A total of 22 factories were affected, including facilities that process shrimp and make footwear for Nike and Adidas. Here are more details.

5. A series of grim official data released in the US has deepened concerns about the health of the world’s largest economy. Signs of weakness in retail sales and consumer confidence released yesterday suggest Americans are pulling back on spending amid an affordability crisis that has raised pressure on Trump.

• ‘Scraping for crumbs’: High prices of food, rents and healthcare are forcing lower-income Americans to cut back on necessities just as the Trump administration curbs government supports.

The Big Read

Ireland’s location on Europe’s western fringe has made it pivotal to global communications. But with a small navy and limited intelligence sharing, some say the military-neutral EU member is incapable of protecting itself and the transatlantic data cables running through its territory. Read more on what makes the island nation a weak link in EU defence.

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• Rare earths: A small Australian town best known for its giant Elvis Presley festival is gaining global significance in the race to break China’s control of rare earths.

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The Fractured Age, a new book by Neil Shearing of Capital Economics, argues that the world economy will divide between a US-centred bloc and a China-centred one. But which country will come out ahead? Martin Wolf examines this question in his latest column.

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Net revenue and EPS results
HP Inc. and its subsidiaries (“HP”) announced fiscal 2025 net revenue of $55.3 billion, up 3.2% (up 3.7% in constant currency) from the prior-year period.
 
Fiscal 2025 GAAP diluted net EPS was $2.65, down from $2.81 in the prior-year. Fiscal 2025 non-GAAP diluted net EPS was $3.12, down from $3.43 in the prior-year period. Fiscal 2025 non-GAAP net earnings and non-GAAP diluted net EPS exclude after-tax adjustments of $443 million, or $0.47 per diluted share, related to restructuring and other charges, acquisition and divestiture charges, amortization of intangible assets, certain litigation charges (benefits), net, non-operating retirement-related credits, tax adjustments, and the related tax impact on these items.
 
Fourth quarter net revenue was $14.6 billion, up 4.2% (up 3.8% in constant currency) from the prior-year period.
 
Fourth quarter GAAP diluted net EPS was $0.84, down from $0.93 in the prior-year period and within the previously provided outlook of $0.75 to $0.85. Fourth quarter non-GAAP diluted net EPS was $0.93, down from $0.96 in the prior-year period and within the previously provided outlook of $0.87 to $0.97. Fourth quarter non-GAAP net earnings and non-GAAP diluted net EPS excludes after-tax adjustments of $82 million, or $0.09 per diluted share, related to restructuring and other charges, acquisition and divestiture charges, amortization of intangible assets, certain litigation charges (benefits), net, tax adjustments, and the related tax impact on these items.
 
“HP’s strategy to lead the Future of Work continues to deliver strong performance, marked by our sixth consecutive quarter of revenue growth,” said Enrique Lores, President and CEO of HP Inc. “Our FY25 results reinforce the power of our portfolio and the strength of our team in a dynamic environment. As we accelerate innovation across AI-powered devices to drive productivity, security, and flexibility for our customers, our focus for FY26 is on disciplined execution. We are committed to driving measurable results – ensuring that our plans translate into long-term value for our shareholders.”
 
“Our FY25 results reflect solid execution in an evolving environment, where we drove strong profit improvement in the back half of the year and returned $1.9 billion dollars to shareholders,” said Karen Parkhill, CFO, HP Inc. “Looking forward, we are taking decisive actions to mitigate recent cost headwinds and are investing in AI-enabled initiatives to accelerate product innovation, improve customer satisfaction, and boost productivity. We are confident these actions will strengthen our foundation and position us for long-term growth.”
 
Asset management
HP generated $3.7 billion in net cash provided by operating activities and $2.9 billion of free cash flow in fiscal 2025. Free cash flow includes net cash provided by operating activities of $3.7 billion adjusted for net investments in leases from integrated financing of $131 million and net investments in property, plant, equipment and purchased intangibles of $897 million. HP utilized $850 million of cash during fiscal 2025 to repurchase approximately 29.5 million shares of common stock in the open market. When combined with the $1.1 billion of cash used to pay dividends, HP returned 66% of its free cash flow to shareholders in fiscal 2025.
 
HP’s net cash provided by operating activities in the fourth quarter of fiscal 2025 was $1.6 billion. Accounts receivable ended the quarter at $5.7 billion, up 2 days quarter over quarter to 35 days. Inventory ended the quarter at $8.5 billion, down 2 days quarter over quarter to 66 days. Accounts payable ended the quarter at $18.1 billion, up 1 day quarter over quarter to 139 days.
 
HP generated $1.5 billion of free cash flow in the fourth quarter. Free cash flow includes net cash provided by operating activities of $1.6 billion adjusted for net investments in leases from integrated financing of $60 million and net investments in property, plant and equipment of $197 million.
 
HP’s dividend payment of $0.2894 per share in the fourth quarter resulted in cash usage of $270 million. HP also utilized $500 million of cash during the quarter to repurchase approximately 18.3 million shares of common stock in the open market. HP exited the quarter with $3.7 billion in gross cash, which includes cash and cash equivalents of $3.7 billion, restricted cash of $15 million, cash held for sale of $8 million, and short-term investments of $3 million included in other current assets. Restricted cash is related to amounts collected and held on behalf of a third party for trade receivables previously sold.
 
The HP board of directors has declared a quarterly cash dividend of $0.30 per share on the company’s common stock, payable on January 2, 2026 to stockholders of record as of the close of business on December 11, 2025. This is the first dividend of HP’s 2026 fiscal year.
 
Fiscal 2025 fourth quarter segment results

• Personal Systems net revenue was $10.4 billion, up 8% year over year (up 7% in constant currency) with a 5.8% operating margin. Consumer PS net revenue was up 10% and Commercial PS net revenue was up 7%. Total units were up 7% with Consumer PS units up 8% and Commercial PS units up 7%.

• Printing net revenue was $4.3 billion, down 4% year over year (down 4% in constant currency) with an 18.9% operating margin. Consumer Printing net revenue was down 9% and Commercial Printing net revenue was down 4%. Supplies net revenue was down 4% (down 3% in constant currency). Total hardware units were down 12%, with both Consumer and Commercial Printing units reflecting similar declines.

 
Outlook
For the fiscal 2026 first quarter, HP estimates GAAP diluted net EPS to be in the range of $0.58 to $0.66 and non-GAAP diluted net EPS to be in the range of $0.73 to $0.81. Fiscal 2026 first quarter non-GAAP diluted net EPS estimates exclude $0.15 per diluted share, primarily related to restructuring and other charges, acquisition and divestiture charges, amortization of intangible assets, non-operating retirement-related credits, tax adjustments, and the related tax impact on these items.
 
For fiscal 2026, HP estimates GAAP diluted net EPS to be in the range of $2.47 to $2.77 and non-GAAP diluted net EPS to be in the range of $2.90 to $3.20. Fiscal 2026 non-GAAP diluted net EPS estimates exclude $0.43 per diluted share, primarily related to restructuring and other charges, acquisition and divestiture charges, amortization of intangible assets, non-operating retirement-related credits, tax adjustments, and the related tax impact on these items. For fiscal 2026, HP anticipates generating free cash flow in the range of $2.8 to $3.0 billion.  HP’s outlook reflects the added cost driven by the current U.S. trade-related regulations in place, and associated mitigations. 
 
More information on HP’s earnings, including additional financial analysis and an earnings overview presentation, is available on HP’s Investor Relations website at investor.hp.com.
 
HP’s FY25 Q4 earnings conference call is accessible via audio webcast at www.hp.com/investor/2025Q4Webcast.
 
Company-wide initiative announced in November 2025
Today, HP Inc. announced a company-wide initiative (“fiscal 2026 plan”) to drive customer satisfaction, product innovation, and productivity through artificial intelligence adoption and enablement. The company estimates that these actions will result in gross run rate savings of approximately $1 billion by the end of fiscal 2028. The company estimates that it will incur approximately $650 million in labor and non-labor costs related to restructuring and other charges, with approximately $250 million in fiscal 2026. The company expects to reduce gross global headcount by approximately 4,000-6,000 employees. These actions are expected to be completed by the end of fiscal 2028.
 
About HP Inc.
HP Inc. (NYSE:HPQ) is a global technology leader redefining the Future of Work. Operating in more than 180 countries, HP delivers innovative and AI-powered devices, software, services and subscriptions that drive business growth and professional fulfillment. For more information, please visit: HP.com.
 
Use of non-GAAP financial information
To supplement HP’s consolidated condensed financial statements presented on a generally accepted accounting principles (“GAAP”) basis, HP provides net revenue on a constant currency basis, non-GAAP total operating expense, non-GAAP operating profit, non-GAAP operating margin, non-GAAP other income and expenses, non-GAAP tax rate, non-GAAP net earnings, non-GAAP diluted net EPS, free cash flow, gross cash and net cash (debt) financial measures. HP also provides forecasts of non-GAAP diluted net EPS and free cash flow. Reconciliations of these non-GAAP financial measures to the most directly comparable GAAP financial measures are included in the tables below or elsewhere in the materials accompanying this news release. In addition, an explanation of the ways in which HP’s management uses these non-GAAP measures to evaluate its business, the substance behind HP’s decision to use these non-GAAP measures, the material limitations associated with the use of these non-GAAP measures, the manner in which HP’s management compensates for those limitations, and the substantive reasons why HP’s management believes that these non-GAAP measures provide useful information to investors is included under “Use of non-GAAP financial measures” after the tables below. This additional non-GAAP financial information is not meant to be considered in isolation or as a substitute for net revenue, operating expense, operating profit, operating margin, other income and expenses, tax rate, net earnings, diluted net EPS, cash provided by operating activities or cash, cash equivalents, and restricted cash prepared in accordance with GAAP.
 
Forward-looking statements
This document contains forward-looking statements based on current expectations and assumptions that involve risks and uncertainties. If the risks or uncertainties ever materialize or the assumptions prove incorrect, they could affect the business and results of operations of HP Inc. and its consolidated subsidiaries which may differ materially from those expressed or implied by such forward-looking statements and assumptions.
 
All statements other than statements of historical fact are statements that could be deemed forward-looking statements, including, but not limited to, projections of net revenue, margins, expenses, effective tax rates, net earnings, net earnings per share, cash flows, benefit plan funding, deferred taxes, share repurchases, foreign currency exchange rates or other financial items; any projections of the amount, timing or impact of cost savings or restructuring and other charges, planned structural cost reductions and productivity initiatives; any statements of the plans, strategies and objectives of management for future operations, including, but not limited to, our business model and transformation, our sustainability goals, our go-to-market strategy, the execution of restructuring plans and any resulting cost savings (including the fiscal 2023 plan and the fiscal 2026 plan), net revenue or profitability improvements or other financial impacts; any statements concerning the expected development, demand, performance, market share or competitive performance relating to products or services; any statements concerning potential supply constraints, component shortages, manufacturing disruptions or logistics challenges; any statements regarding current or future macroeconomic trends or events, including global trade policies, and the impact of those trends and events on HP and its financial performance; any statements regarding pending investigations, claims, disputes or other litigation matters; any statements of expectation or belief as to the timing and expected benefits of acquisitions and other business combination and investment transactions; and any statements of assumptions underlying any of the foregoing.  Forward-looking statements can also generally be identified by words such as “future,” “anticipates,” “believes,” “estimates,” “expects,” “intends,” “plans,” “predicts,” “projects,” “will,” “would,” “could,” “can,” “may,” and similar terms.
 
Risks, uncertainties and assumptions that could affect our business and results of operations include factors relating to HP’s ability to execute on its strategic plans, including the previously announced initiatives, business model changes and transformation; the development and transition of new products and services and the enhancement of existing products and services to meet evolving customer needs and respond to emerging technological trends, including artificial intelligence; the use of artificial intelligence; the impact of macroeconomic and geopolitical trends, changes and events, including global trade policies, the ongoing military conflict in Ukraine, continued instability in the Middle East or tensions in the Taiwan Strait and South China Sea and the regional and global ramifications of these events; volatility in global capital markets and foreign currency, changes in benchmark interest rates, the effects of inflation and instability of financial institutions; risks associated with HP’s international operations and the effects of business disruption events, including those resulting from climate change; the need to manage (and reliance on) third-party suppliers, including with respect to supply constraints and component shortages, and the need to manage HP’s global, multi-tier distribution network and potential misuse of pricing programs by HP’s channel partners, adapt to new or changing marketplaces and effectively deliver HP’s services; the execution and performance of contracts by HP and its suppliers, customers, clients and partners, including logistical challenges with respect to such execution and performance; the competitive pressures faced by HP’s businesses; the impact of third-party claims of IP infringement; successfully innovating, developing and executing HP’s go-to-market strategy, including online, omnichannel and contractual sales, in an evolving distribution, reseller and customer landscape; successfully competing and maintaining the value proposition of HP’s products, including supplies and services; challenges to HP’s ability to accurately forecast inventories, demand and pricing, which may be due to HP’s multi-tiered channel, sales of HP’s products to unauthorized resellers or unauthorized resale of HP’s products or our uneven sales cycle; the hiring and retention of key employees; the results of our restructuring plans (including the fiscal 2023 plan and the fiscal 2026 plan), including estimates and assumptions related to the cost (including any possible disruption of HP’s business) and the anticipated benefits of our restructuring plans; the protection of HP’s intellectual property assets, including intellectual property licensed from third parties; disruptions in operations from system security risks, data protection breaches, or cyberattacks; HP’s ability to maintain its credit rating, satisfy its debt obligations and complete any contemplated share repurchases, other capital return programs or other strategic transactions; changes in estimates and assumptions HP makes in connection with the preparation of its financial statements; the impact of changes to federal, state, local and foreign laws and regulations, including environmental regulations and tax laws; integration and other risks associated with business combination and investment transactions; our aspirations related to environmental and societal matters; potential impacts, liabilities and costs from pending or potential investigations, claims and disputes; the effectiveness of our internal control over financial reporting; and other risks that are described in HP’s Annual Report on Form 10-K for the fiscal year ended October 31, 2024 and HP’s other filings with the Securities and Exchange Commission (“SEC”). HP’s fiscal 2023 plan included HP’s efforts to take advantage of future growth opportunities, including but not limited to, investments to drive growth, investments in our people, improving product mix, driving structural cost savings and other productivity measures. HP’s fiscal 2026 plan includes HP’s efforts to drive customer satisfaction, product innovation, and productivity through artificial intelligence adoption and enablement, and cost savings associated with the fiscal 2026 plan represent gross reductions in costs from these restructuring plans. Structural cost savings represent gross reductions in costs driven by operational efficiency, digital transformation, and portfolio optimization. These initiatives include but are not limited to workforce reductions, platform simplification, programs consolidation and productivity measures undertaken by HP, which HP expects to be sustainable in the longer-term. These structural cost savings are net of any new recurring costs resulting from these initiatives and exclude one-time investments to generate such savings. HP’s expectations on the longer-term sustainability of such structural cost savings are based on its current business operations and market dynamics and could be significantly impacted by various factors, including but not limited to HP’s evolving business models, future investment decisions, market environment and technology landscape.
 
As in prior periods, the financial information set forth in this document, including any tax-related items, reflects estimates based on information available at this time. While HP believes these estimates to be reasonable, these amounts could differ materially from reported amounts in HP’s Annual Report on Form 10-K for the fiscal year ending October 31, 2025 and October 31, 2026, Quarterly Report on Form 10-Q for the fiscal quarter ending January 31, 2026 and HP’s other filings with the SEC. The forward-looking statements in this document are made as of the date of this document and HP assumes no obligation and does not intend to update these forward-looking statements. 
 
HP’s Investor Relations website at investor.hp.com contains a significant amount of information about HP, including financial and other information for investors. HP encourages investors to visit its website from time to time, as information is updated, and new information is posted. The content of HP’s website is not incorporated by reference into this document or in any other report or document HP files with the SEC, and any references to HP’s website are intended to be inactive textual references only. 
 
 
Available in PDF format — including all tables

As AI tools advance, multinational employers are grappling with a growing patchwork of governance frameworks. Notably in the Asia-Pacific (APAC) region, Japan, Singapore, India, and Australia are making moves that encourage AI use while seeking to curb the risks. Businesses operating in these locations should stay on top of recent developments and create an action plan to address the nuances in each applicable location. Here’s a breakdown of new developments in this region and what you can do to prepare as AI regulatory frameworks continue to take shape.

Japan Focuses on Innovation

Earlier this year, the legislature in Japan passed a bill that encourages research and development – as well as advancements – in AI technology and commits to international leadership and cooperation. The new law sets expectations for businesses to cooperate with key principles for AI governance, including transparency and risk mitigation. Research institutions are expected to work on AI development and train skilled workers, and citizens are encouraged to learn more about AI. There are no sanctions for noncompliance. Rather, the focus is on voluntary collaboration and making strides in this realm.

Moreover, the law charges Japan’s national government with creating AI policies. Specifically, it establishes an Artificial Intelligence Strategy Headquarters within the Cabinet, which will oversee the new Artificial Intelligence Basic Plan and coordinate all AI policy. Local governments are responsible for implementing a regional plan.

Singapore Highlights Testing and Sector-Specific Compliance

Balancing innovation with effective risk management is also a priority in Singapore. The government published its first Model AI Governance Framework in 2019 – which established principles for ethical AI use – and has invested millions in AI efforts. This year, through AI Verify Foundation, Singapore launched the Global AI Assurance Pilot, which served as a sandbox to test Generative AI applications and establish international standards. The purpose was “to help codify emerging norms and best practices around technical testing of GenAI applications.”

In terms of regulations, Singapore has chosen not to pursue a comprehensive AI statute. Instead, it continues to follow a sector-specific regulatory model that addresses risks through existing frameworks for finance, healthcare, employment, and other compliance areas.

India Issues Robust Guidelines

India released its AI Governance Guidelines in 2025, emphasizing safety, trust, and flexibility. Specifically, the seven core principles are:

1. Trust is the Foundation
2. People First
3. Innovation over Restraint
4. Fairness and Equity
5. Accountability
6. Understandable by Design
7. Safety, Resilience, and Sustainability

The guidelines, which you can learn about in more detail here, make six key recommendations, including education and skills training, as well as policies that support innovation, mitigate risks, and encourage compliance through transparency and voluntary measures.

The guidelines also recommend setting up an AI Governance Group, which would be supported by a Technology and Policy Expert Committee – and enabling the AI Safety Institute to provide technical expertise on trust and safety issues.

As in most other countries, the recommendations also call for sector-specific regulations to continue. For example, the Digital Personal Data Protection Act (DPDP Act), India’s first comprehensive data privacy legislation regulating digital personal data processing, governs the use of personal data to train AI models.

Australia Shifts Gears

In 2024, Australia initially proposed mandatory guardrails for AI use in high-risk settings and released its Voluntary AI Safety Standard to serve as guidance while the requirements were pending.

The country has since changed its direction from aiming to roll out a stricter EU-type AI framework to one that looks more like other APAC nations, focused on safe and responsible AI use, as well as innovation and compliance with existing laws and regulations.

Last month, Australia’s National AI Centre issued new Guidance for AI Adoption, which replaces the prior voluntary standard and sets out six practices for responsible AI governance:

1. Decide who is accountable 
2. Understand impacts and plan accordingly 
3. Measure and manage risks
4. Share essential information
5. Test and monitor
6. Maintain human control

To learn more, you can visit the government’s resource page.

Key Takeaways for Multinational Businesses

• Global AI Regulations Are Evolving Quickly: While Japan, Singapore, India, and Australia have all introduced frameworks that balance innovation with risk mitigation, the European Union has taken a stricter approach and leading European companies are pushing back. You’ll want to track developments closely.
• No One-Size-Fits-All Plan: Carefully review the applicable countries’ approach to voluntary vs. mandatory guidance and sector-specific rules vs. comprehensive AI statutes.
• Themes Are Emerging: Although each approach is different, goals like safety, transparency, accountability, and human oversight are common.
• Existing Laws Still Apply: AI regulatory frameworks generally do not replace compliance obligations under data privacy, finance, healthcare, employment, and other laws that may apply when AI tools are used. Be sure that your AI, Tech, Legal, HR, and other teams coordinate compliance efforts.
• Privacy Rules Still Shape AI Use: Because AI is being handled through existing privacy frameworks, companies should watch how regulators interpret transparency and data use in AI contexts. These factors directly affect how AI models are trained and how they generate outputs.
• Sector-Specific Rules May Affect Certain AI Use Cases More Than Others: Given the region’s reliance on sector-based oversight, areas like recruitment, financial services, and healthcare may face closer scrutiny when AI tools are used in those contexts.
• Navigating AI Governance Can Be Challenging: Having a trusted legal or consulting partner can provide valuable guidance, ensuring compliance and aligning AI practices with business goals. From setting up policies to managing compliance, a partner experienced in AI governance can help prevent costly missteps.

Conclusion

We will continue to monitor legal changes affecting multinational companies, so make sure you are subscribed to Fisher Phillips’ Insight System to receive the latest updates directly to your inbox. If you have questions, contact your Fisher Phillips attorney, the authors of this Insight, or any attorney in our International Practice Group or our AI, Data, and Analytics Practice Group.

Sign up here.

SAP said in a statement that it is “committed to the highest standards of business ethics and respects the intellectual property rights of others.”

“o9 believes that the evidence of SAP’s coordinated attack on o9 is clear and compelling,” o9 CEO Chakri Gottemukkala said in a statement.

Dallas-based o9 specializes in artificial intelligence-powered business planning software. The lawsuit said that SAP had lost customers to competitors after it introduced a new version of its enterprise planning and supply chain management software with “high costs, [a] long timeline, operational risks, and poor implementation.”

The lawsuit said that three o9 executives, all based in the Netherlands, left for SAP this year and took more than 22,000 files with them that included confidential technical, marketing and sales information about o9’s supply-chain software.

The complaint said that SAP has since altered its software to “closely mimic” o9’s products.

“It has now become evident that SAP wishes to displace o9 as the leader in the advanced business planning solutions space by attempting to copy o9’s platform architecture, its capabilities, and its product messaging strategies,” o9 said.

o9 requested a court order blocking SAP from misusing its trade secrets and unspecified monetary damages.

The case is o9 Solutions Inc v. SAP SE, U.S. District Court for the Northern District of Texas, No. 3:25-cv-03245.

For o9: Taj Clayton, Adam Alper, Michael De Vries, Carson Young and Christopher Lawless of Kirkland & Ellis

For SAP: attorney information not yet available

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