Agentic AI in healthcare is graduating from answering prompts to autonomously executing complex marketing tasks – and life sciences companies are betting their commercial strategies on it.

According to a recent report cited by Capgemini Invent, AI agents could generate up to $450 billion in economic value through revenue uplift and cost savings globally by 2028, with 69% of executives planning to deploy agents in marketing processes by year’s end.

The stakes are particularly high in pharmaceutical marketing, where sales representatives have increasingly limited face-time with healthcare professionals (HCPs) – a trend accelerated by Covid-19. The challenge isn’t just access; it’s making those rare interactions count with intelligence that’s currently trapped in data silos.

The fragmented intelligence problem

Briggs Davidson, senior director of digital, data & marketing strategy for life Sciences at Capgemini Invent, outlines a scenario that will sound familiar to anyone in pharma marketing: An HCP attends a conference where a competitor showcases promising drug results, publishes research, and shifts their prescriptions to a rival product – in a single quarter.

“In most companies, legacy IT infrastructure and data silos keep this information in disparate systems in CRM, events databases and claims data,” Davidson writes. “Chances are, none of that information was accessible to sales reps before they met with the HCP.”

The solution, according to Davidson, isn’t to connect these systems, it’s deploying agentic AI in healthcare marketing to autonomously query, synthesising and acting on unified data. Unlike conversational AI that responds to queries, agentic systems can independently execute multi-step tasks.

Instead of a data engineer building a new pipeline, an AI agent could autonomously query the CRM and claims database to answer business questions like: “Identify oncologists in the Northwest who have a 20% lower prescription volume but attended our last medical congress.”

From orchestration to autonomous execution

Davidson frames the change as moving from an “omnichannel view” – coordinating experiences in channels – to true orchestration powered by agentic AI.

In practice, this means a sales representative could have an agent assist with call and visit planning by asking: “What messages has my HCP responded to most recently?” or “Can you create a detailed intelligence brief on my HCP?”

The agentic system would compile:

• Their most recent conversation with the HCP,
• The HCP’s prescribing behaviour,
• Thought-leaders the HCP follows,
• Relevant content to share,
• The HCP’s preferred outreach channels (in-person visits, emails, webinars).

More significantly, the AI agent would then create a custom call plan for each HCP based on their unified profile and recommend follow-up steps based on engagement outcomes. “Agentic AI systems are about driving action, graduating from ‘answer my prompt,’ to ‘autonomously execute my task,'” Davidson explains.

“That means evolving the sales representative mindset from asking questions to coordinating small teams of specialised agents that work together: one plans, another retrieves and checks content, a third schedules and measures, and a fourth enforces compliance guardrails – all under human oversight.”

The AI-ready data prerequisite

The operational promise hinges on what Davidson calls “AI-ready data” – standardised, accessible, complete, and trustworthy information that enables three abilities:

Faster decision making: Predictive analytics that provide near real-time alerts on what’s about to happen, letting sales representatives act proactively.

Personalisation at scale: Delivering customised experiences to thousands of HCPs simultaneously with small human teams enabled by specialised agent networks.

True marketing ROI: Moving beyond monthly historical reports to understanding which marketing activities are actively driving prescriptions.

Davidson emphasises that successful deployment starts with marketing and IT alignment on initial use cases, with stakeholders identifying KPIs that demonstrate tangible outcomes – like specific percentage increases in HCP engagement or sales representative productivity.

Critical implementation questions

The article frames agentic AI in healthcare as “not simply another technology-led ability; it’s a new operating layer for commercial teams.” But it acknowledges that “agentic AI’s full value only materialises with AI-ready data, trustworthy deployment and workflow redesign.”

What remains unaddressed is the regulatory and compliance complexity of autonomous systems querying claims databases containing prescriber behaviour, particularly under HIPAA’s minimum necessary standard. The piece also doesn’t detail actual client implementations or metrics beyond the aspirational $450B economic value projection.

For global organisations, Davidson says use cases “can and should be tailored to fit each market’s maturity for maximum ROI,” suggesting that deployment will vary in regulatory environments. The fundamental value proposition, according to Davidson, centres on bidirectional benefit: “The HCP receives directly relevant content, and the marketing teams can drive increased HCP engagement and conversion.”

Whether that vision of autonomous marketing agents coordinating in CRM, events, and claims systems becomes standard practice by 2028 – or remains constrained by data governance realities – will likely determine if life sciences achieves anything close to that $450 billion opportunity.

See also: China’s hyperscalers bet billions on agentic AI as commerce becomes the new battleground

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

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OpenAI, Google, and Anthropic announced specialised medical AI capabilities within days of each other this month, a clustering that suggests competitive pressure rather than coincidental timing. Yet none of the releases are cleared as medical devices, approved for clinical use, or available for direct patient diagnosis—despite marketing language emphasising healthcare transformation.

OpenAI introduced ChatGPT Health on January 7, allowing US users to connect medical records through partnerships with b.well, Apple Health, Function, and MyFitnessPal. Google released MedGemma 1.5 on January 13, expanding its open medical AI model to interpret three-dimensional CT and MRI scans alongside whole-slide histopathology images. 

Anthropic followed on January 11 with Claude for Healthcare, offering HIPAA-compliant connectors to CMS coverage databases, ICD-10 coding systems, and the National Provider Identifier Registry.

All three companies are targeting the same workflow pain points—prior authorisation reviews, claims processing, clinical documentation—with similar technical approaches but different go-to-market strategies.

Developer platforms, not diagnostic products

The architectural similarities are notable. Each system uses multimodal large language models fine-tuned on medical literature and clinical datasets. Each emphasises privacy protections and regulatory disclaimers. Each positions itself as supporting rather than replacing clinical judgment.

The differences lie in deployment and access models. OpenAI’s ChatGPT Health operates as a consumer-facing service with a waitlist for ChatGPT Free, Plus, and Pro subscribers outside the EEA, Switzerland, and the UK. Google’s MedGemma 1.5 releases as an open model through its Health AI Developer Foundations program, available for download via Hugging Face or deployment through Google Cloud’s Vertex AI. 

Anthropic’s Claude for Healthcare integrates into existing enterprise workflows through Claude for Enterprise, targeting institutional buyers rather than individual consumers. The regulatory positioning is consistent across all three. 

OpenAI states explicitly that Health “is not intended for diagnosis or treatment.” Google positions MedGemma as “starting points for developers to evaluate and adapt to their medical use cases.” Anthropic emphasises that outputs “are not intended to directly inform clinical diagnosis, patient management decisions, treatment recommendations, or any other direct clinical practice applications.”

Benchmark performance vs clinical validation

Medical AI benchmark results improved substantially across all three releases, though the gap between test performance and clinical deployment remains significant. Google reports that MedGemma 1.5 achieved 92.3% accuracy on MedAgentBench, Stanford’s medical agent task completion benchmark, compared to 69.6% for the previous Sonnet 3.5 baseline. 

The model improved by 14 percentage points on MRI disease classification and 3 percentage points on CT findings in internal testing. Anthropic’s Claude Opus 4.5 scored 61.3% on MedCalc medical calculation accuracy tests with Python code execution enabled, and 92.3% on MedAgentBench. 

The company also claims improvements in “honesty evaluations” related to factual hallucinations, though specific metrics were not disclosed. 

OpenAI has not published benchmark comparisons for ChatGPT Health specifically, noting instead that “over 230 million people globally ask health and wellness-related questions on ChatGPT every week” based on de-identified analysis of existing usage patterns.

These benchmarks measure performance on curated test datasets, not clinical outcomes in practice. Medical errors can have life-threatening consequences, translating benchmark accuracy to clinical utility more complex than in other AI application domains.

Regulatory pathway remains unclear

The regulatory framework for these medical AI tools remains ambiguous. In the US, the FDA’s oversight depends on intended use. Software that “supports or provides recommendations to a health care professional about prevention, diagnosis, or treatment of a disease” may require premarket review as a medical device. None of the announced tools has FDA clearance.

Liability questions are similarly unresolved. When Banner Health’s CTO Mike Reagin states that the health system was “drawn to Anthropic’s focus on AI safety,” this addresses technology selection criteria, not legal liability frameworks. 

If a clinician relies on Claude’s prior authorisation analysis and a patient suffers harm from delayed care, existing case law provides limited guidance on responsibility allocation.

Regulatory approaches vary significantly across markets. While the FDA and Europe’s Medical Device Regulation provide established frameworks for software as a medical device, many APAC regulators have not issued specific guidance on generative AI diagnostic tools. 

This regulatory ambiguity affects adoption timelines in markets where healthcare infrastructure gaps might otherwise accelerate implementation—creating a tension between clinical need and regulatory caution.

Administrative workflows, not clinical decisions

Real deployments remain carefully scoped. Novo Nordisk’s Louise Lind Skov, Director of Content Digitalisation, described using Claude for “document and content automation in pharma development,” focused on regulatory submission documents rather than patient diagnosis. 

Taiwan’s National Health Insurance Administration applied MedGemma to extract data from 30,000 pathology reports for policy analysis, not treatment decisions.

The pattern suggests institutional adoption is concentrating on administrative workflows where errors are less immediately dangerous—billing, documentation, protocol drafting—rather than direct clinical decision support where medical AI capabilities would have the most dramatic impact on patient outcomes.

Medical AI capabilities are advancing faster than the institutions deploying them can navigate regulatory, liability, and workflow integration complexities. The technology exists. The US$20 monthly subscription provides access to sophisticated medical reasoning tools. 

Whether that translates to transformed healthcare delivery depends on questions these coordinated announcements leave unaddressed.

See also: AstraZeneca bets on in-house AI to speed up oncology research

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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Apple’s multi-year agreement to integrate Google’s Gemini models into its revamped Siri offers a rare window into how one of the world’s most selective technology companies evaluates foundation models – and the criteria should matter to any enterprise weighing similar decisions.

The stakes were considerable. Apple had been publicly integrating ChatGPT into its devices since late 2024, giving OpenAI prominent positioning in the Apple Intelligence ecosystem.

Google’s Gemini win represents a shift in Apple’s AI infrastructure strategy, one that relegates OpenAI to what Parth Talsania, CEO of Equisights Research, describes as “a more supporting role, with ChatGPT remaining positioned for complex, opt-in queries rather than the default intelligence layer.”

The evaluation that mattered

Apple’s reasoning was notably specific. “After careful evaluation, Apple determined Google’s AI technology provides the most capable foundation for Apple Foundation Models,” according to the joint statement. The phrasing matters – Apple didn’t cite partnership convenience, pricing, or ecosystem compatibility. The company framed this explicitly as a capabilities assessment.

Apple’s evaluation criteria likely mirrored concerns familiar to any organisation building AI into core products: model performance at scale, inference latency, multimodal capabilities, and crucially, the ability to run models both on-device and in cloud environments while maintaining privacy standards.

Google’s technology already powers Samsung’s Galaxy AI in millions of devices, providing proven deployment evidence at consumer scale. But Apple’s decision unlocks something different: integration in more than two billion active devices, with the technical demands that come with Apple’s performance and privacy requirements.

What has changed since ChatGPT integration

The timing raises questions. Apple rolled out ChatGPT integration just over a year ago, positioning Siri to tap into the chatbot for complex queries. The company now states, “there were no major changes to the ChatGPT integration at the time,” but the competitive dynamics have clearly shifted.

OpenAI’s response to Google’s Gemini 3 release in late 2025 – what reports described as a “code red” to accelerate development – suggests the competitive pressure was real. For enterprises, this highlights a risk often under-weighted in vendor selection: the pace of model capability advancement varies significantly between providers, and today’s leader may not maintain that position in a multi-year deployment.

Apple’s choice of a multi-year agreement with Google, rather than maintaining flexibility to switch between providers, suggests confidence in Google’s development trajectory. That’s a bet on sustained R&D investment, continued model improvements, and infrastructure scaling – the same factors enterprise buyers need to assess beyond current benchmarks.

The infrastructure question

The deal raises immediate concerns about concentration. “The seems like an unreasonable concentration of power for Google, given that they also have Android and Chrome,” Tesla CEO Elon Musk posted on social media. The critique reflects a legitimate enterprise concern about vendor dependency.

Google now powers AI features in both major mobile operating systems through different mechanisms: directly via Android, and through this partnership for iOS. For enterprises deploying AI capabilities, the parallel is that relying on a single foundation model provider creates technical and commercial dependencies that extend beyond the immediate integration.

This makes Apple’s architectural approach worth examining. The company emphasised that “Apple Intelligence will continue to run on Apple devices and Private Cloud Compute, while maintaining Apple’s industry-leading privacy standards.”

The hybrid deployment model – on-device processing for privacy-sensitive operations, cloud-based models for complex tasks – offers a template for enterprises balancing capability with data governance requirements.

Market implications beyond mobile

The deal’s immediate impact was measurable: Alphabet’s market valuation crossed US$4 trillion on Monday, with the stock having jumped 65% in 2024 on growing investor confidence in its AI efforts. But the strategic implications extend beyond market caps.

Google has been methodically building positions in the AI stack – frontier models, image and video generation, and now default integration into iOS devices. For enterprises, this vertical integration matters when evaluating cloud AI services: a provider’s foundation model capabilities increasingly connect to their broader infrastructure, tools, and ecosystem positioning.

Apple’s setbacks on the AI front – delayed Siri upgrades, executive changes, lukewarm reception for initial generative AI tools – are instructive from another angle. Even companies with enormous resources and talent can struggle with AI product execution. The decision to partner with Google rather than persist with entirely proprietary development acknowledges the complexity and resource demands of frontier model development.

The search revenue connection

The Gemini deal builds on an existing commercial relationship that generates tens of billions in annual revenue for Apple: Google pays to remain the default search engine on Apple devices. That arrangement has faced regulatory scrutiny, but it establishes precedent for deep technical integration between the companies.

The search deal likely influenced negotiations around the Gemini integration, just as existing vendor relationships shape enterprise AI procurement. Those relationships can be advantages – established trust, proven integration capabilities – or constraints that limit evaluation of alternatives.

The OpenAI question

The deal leaves OpenAI in an awkward position. ChatGPT remains available on Apple devices, but as an optional feature rather than the infrastructure layer. For a company that has positioned itself as the AI leader, losing default integration to Google represents a strategic setback.

The competitive dynamic offers a reminder that the foundation model market remains fluid. Provider positioning can shift quickly, and exclusive relationships between major players can reshape options for everyone else. Maintaining options – through abstraction layers, multi-model strategies, or portable architectures – becomes more valuable in rapidly evolving markets.

What comes next

Google stated that Gemini models will power not just the revamped Siri coming later this year, but “other future Apple Intelligence features.” The scope of integration will likely expand as Apple builds out its AI capabilities, creating deeper technical dependencies and raising the stakes of the partnership.

The financial terms remain undisclosed, leaving the question of how Apple and Google structure pricing for this scale of deployment? Enterprise buyers negotiating foundation model licensing will be watching for any signals about how such deals get priced at a massive scale.

Apple’s decision doesn’t make Google’s Gemini the obvious choice for every enterprise – far from it. But the deal does offer validated evidence of what one extremely selective technology company prioritised when evaluating foundation models under demanding requirements. For enterprise AI buyers navigating their own evaluations, that’s a signal worth considering amid the noise of vendor marketing and benchmark leader boards.

See also: Apple plans big Siri update with help from Google AI

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 co-located with other leading technology events. Click here for more information.

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Enterprise AI deployment faces a fundamental tension: organisations need sophisticated language models but baulk at the infrastructure costs and energy consumption of frontier systems.

NTT’s recent launch of tsuzumi 2, a lightweight large language model (LLM) running on a single GPU, demonstrates how businesses are resolving this constraint – with early deployments showing performance matching larger models and running at a fraction of the operational cost.

The business case is straightforward. Traditional large language models require dozens or hundreds of GPUs, creating electricity consumption and operational cost barriers that make AI deployment impractical for many organisations.

For enterprises operating in markets with constrained power infrastructure or tight operational budgets, these requirements eliminate AI as a viable option. NTT’s press release illustrates the practical considerations driving lightweight LLM adoption with Tokyo Online University’s deployment.

The university operates an on-premise platform keeping student and staff data in its campus network – a data sovereignty requirement common in educational institutions and regulated industries.

After validating that tsuzumi 2 handles complex context understanding and long-document processing at production-ready levels, the university deployed it for course Q&A enhancement, teaching material creation support, and personalised student guidance.

The single-GPU operation means the university avoids both capital expenditure for GPU clusters and ongoing electricity costs. More significantly, on-premise deployment addresses data privacy concerns that prevent many educational institutions from using cloud-based AI services that process sensitive student information.

Performance without scale: The technical economics

NTT’s internal evaluation for financial-system inquiry handling showed tsuzumi 2 matching or exceeding leading external models despite dramatically smaller infrastructure requirements. The performance-to-resource ratio determines AI adoption feasibility for enterprises where the total cost of ownership drives decisions.

The model delivers what NTT characterises as “world-top results among models of comparable size” in Japanese language performance, with particular strength in business domains prioritising knowledge, analysis, instruction-following, and safety.

For enterprises operating primarily in Japanese markets, this language optimisation reduces the need to deploy larger multilingual models requiring significantly more computational resources.

Reinforced knowledge in financial, medical, and public sectors – developed based on customer demand – enables domain-specific deployments without extensive fine-tuning.

The model’s RAG (Retrieval-Augmented Generation) and fine-tuning capabilities allow efficient development of specialised applications for enterprises with proprietary knowledge bases or industry-specific terminology where generic models underperform.

Data sovereignty and security as business drivers

Beyond cost considerations, data sovereignty drives lightweight LLM adoption in regulated industries. Organisations handling confidential information face risk exposure when processing data through external AI services subject to foreign jurisdiction.

NTT positions tsuzumi 2 as a “purely domestic model” developed from scratch in Japan, operating on-premises or in private clouds. This addresses concerns prevalent in Asia-Pacific markets about data residency, regulatory compliance, and information security.

FUJIFILM Business Innovation’s partnership with NTT DOCOMO BUSINESS demonstrates how enterprises combine lightweight models with existing data infrastructure. FUJIFILM’s REiLI technology converts unstructured corporate data – contracts, proposals, mixed text and images – into structured information.

Integrating tsuzumi 2’s generative capabilities enables advanced document analysis without transmitting sensitive corporate information to external AI providers. This architectural approach – combining lightweight models with on-premise data processing – represents a practical enterprise AI strategy balancing capability requirements with security, compliance, and cost constraints.

Multimodal capabilities and enterprise workflows

tsuzumi 2 includes built-in multimodal support handling text, images, and voice in enterprise applications. Thematters for business workflows requiring AI to process multiple data types without deploying separate specialised models.

Manufacturing quality control, customer service operations, and document processing workflows typically involve text, images, and sometimes voice inputs. Single models handling all three reduce integration complexity compared to managing multiple specialised systems with different operational requirements.

Market context and implementation considerations

NTT’s lightweight approach contrasts with hyperscaler strategies emphasising massive models with broad capabilities. For enterprises with substantial AI budgets and advanced technical teams, frontier models from OpenAI, Anthropic, and Google provide cutting-edge performance.

However, this approach excludes organisations lacking these resources – a significant portion of the enterprise market, particularly in Asia-Pacific regions with varying infrastructure quality. Regional considerations matter.

Power reliability, internet connectivity, data centre availability, and regulatory frameworks vary significantly in markets. Lightweight models enabling on-premise deployment accommodate these variations better than approaches requiring consistent cloud infrastructure access.

Organisations evaluating lightweight LLM deployment should consider several factors:

Domain specialisation: tsuzumi 2’s reinforced knowledge in financial, medical, and public sectors addresses specific domains, but organisations in other industries should evaluate whether available domain knowledge meets their requirements.

Language considerations: Optimisation for Japanese language processing benefits Japanese-market operations but may not suit multilingual enterprises requiring consistent cross-language performance.

Integration complexity: On-premise deployment requires internal technical capabilities for installation, maintenance, and updates. Organisations lacking these capabilities may find cloud-based alternatives operationally simpler despite higher costs.

Performance tradeoffs: While tsuzumi 2 matches larger models in specific domains, frontier models may outperform in edge cases or novel applications. Organisations should evaluate whether domain-specific performance suffices or whether broader capabilities justify higher infrastructure costs.

The practical path forward?

NTT’s tsuzumi 2 deployment demonstrates that sophisticated AI implementation doesn’t require hyperscale infrastructure – at least for organisations whose requirements align with lightweight model capabilities. Early enterprise adoptions show practical business value: reduced operational costs, improved data sovereignty, and production-ready performance for specific domains.

As enterprises navigate AI adoption, the tension between capability requirements and operational constraints increasingly drives demand for efficient, specialised solutions rather than general-purpose systems requiring extensive infrastructure.

For organisations evaluating AI deployment strategies, the question isn’t whether lightweight models are “better” than frontier systems – it’s whether they’re sufficient for specific business requirements while addressing cost, security, and operational constraints that make alternative approaches impractical.

The answer, as Tokyo Online University and FUJIFILM Business Innovation deployments demonstrate, is increasingly yes.

See also: How Levi Strauss is using AI for its DTC-first business model

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 co-located with other leading technology events. Click here for more information.

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

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