The pharmaceutical industry spends more than $300 billion annually on research and development. The return on that investment, measured as new drugs approved per billion dollars spent, has declined by roughly half every nine years since 1950. [6] This is Eroom's Law, the deliberate inversion of Moore's Law, and it reflects a productivity failure that has persisted through every wave of technological optimism in the sector. Part of the explanation for this failure is scientific complexity. But a substantial and underappreciated part of it is organisational: the intelligence that should be guiding the most consequential decisions in drug development is fragmented across disconnected functions, each operating from an incomplete view of the same landscape.
The clinical team knows the trial data. The HEOR team knows the payer evidence requirements. The competitive intelligence team knows the competitive pipeline. The regulatory team knows the approval landscape. The market access team knows the HTA body expectations. In most pharma organisations, none of these teams has a complete, connected view of all five domains simultaneously, because the tools and data sources they use are separate, the knowledge they generate is stored in separate documents, and the synthesis across all five domains is left to manual coordination that is slow, inconsistent, and impossible to sustain at the speed that competitive therapeutic areas now demand. At Pienomial, we built KnolAI as the multi domain research pharma platform that connects all five intelligence domains in a single governed knowledge layer, and we built it because we have seen the cost of keeping them separate. [9]
1. What Multi-Domain Research Means in Practice
Multi domain research pharma means conducting intelligence analysis that simultaneously draws on clinical, regulatory, competitive, HTA, and commercial evidence domains, synthesised from a single connected knowledge layer, to answer strategic questions that no single domain can answer alone.
A practical example: a HEOR team preparing a NICE single technology appraisal needs to know the clinical evidence from the trial programme, the HTA precedent decisions for analogous products, the comparator landscape from prior NICE and G-BA assessments, the ITC feasibility from the treatment network in the indication, the RWE evidence available to address NICE's specific evidence uncertainties, and the competitive pipeline that will affect the comparator definition by the time the submission lands. That is five intelligence domains, each requiring different source types and different analytical frameworks, each connected to the others in ways that the submission must reflect.
In most pharma organisations today, those five intelligence needs are served by five separate workflows, each running on separate tools or data sources, each producing separate outputs that must be manually reconciled before the submission is built. The reconciliation step, connecting the clinical evidence with the HTA precedent with the comparator landscape with the ITC feasibility with the RWE evidence, is where weeks of analyst time disappear and where inconsistencies between domains create the submission vulnerabilities that HTA reviewers identify.
2. The Five Intelligence Domains That Pharma Research Actually Requires
Every significant commercial decision in pharma, from Phase III protocol design to HTA submission strategy to IRA negotiation preparation, requires intelligence from all five domains. Understanding what each domain contains and why they are interdependent is the starting point for understanding why siloed research is structurally insufficient.
Domain 1, Clinical evidence: Published and unpublished clinical trial results, systematic literature reviews, meta-analyses, network meta-analyses, and real-world evidence studies. This is the domain most HEOR teams handle well with existing tools.
Domain 2, Regulatory landscape: FDA and EMA approval decisions, precedent assessments for analogous products, label evolution across the indication, advisory committee outcomes, and regulatory filing timelines. Most clinical and HEOR teams have limited structured access to this domain and rely on manual searches of public databases.
Domain 3, HTA precedent: NICE appraisal decisions with comparator definitions and evidence challenges, G-BA benefit assessment outcomes with ZVT definitions and IQWiG methodology comments, HAS SMR and ASMR decisions with comparative evidence standards, and ICER evidence report conclusions with uncertainty characterisations. This domain is the one most directly predictive of submission outcome but is the least systematically accessible to most HEOR teams.
Domain 4, Competitive intelligence: Competitor pipeline programmes from clinical trial registries and regulatory filings, competitor regulatory and HTA outcomes, competitor commercial positioning and payer strategies, and early-phase signals from patent filings and conference abstracts. CI teams typically cover parts of this domain but rarely connect their intelligence to the HEOR and regulatory domains in real time.
Domain 5, Commercial and payer intelligence: Formulary decisions, managed care coverage policies, IRA negotiation outcomes and MFP explanation files, outcomes-based contract precedents, and payer-specific evidence frameworks. This domain is typically managed by market access teams using separate tools that do not connect to the clinical or HTA evidence layers.
3. The Organisational Cost of Siloed Research
Research on cross-functional integration in pharma consistently documents the commercial consequences of siloed research. A 2025 analysis found that duplicated technology investments in siloed pharma organisations consume up to 30% of IT budgets, that 40% of new drug launches are delayed due to misalignment between commercial and medical functions, and that weak value propositions result when payer messaging and medical evidence are developed on separate tracks rather than as a connected strategy. Drug development already averages over $2.2 billion per successful asset, and siloed data compounds this cost by leading to repeated experiments, wasted resources, and missed strategic intelligence that could have redirected investment earlier.
The specific cost of research domain silos shows up in three recurring failure patterns. First, late comparator discovery: the HTA comparator for a product is different from the trial comparator because the HTA intelligence was not connected to the clinical protocol design intelligence at Phase II. The correction, if it is even possible, requires an ITC analysis that the trial network may not support. Second, inconsistent cross-submission claims: clinical teams, regulatory teams, and HEOR teams use different versions of the same efficacy data because each team is working from their own domain-specific source set. The inconsistencies create clarification requests that delay approvals. Third, missed competitive signals: a competitor achieves a regulatory or HTA outcome that shifts the evidence bar for the indication, and the clinical and HEOR teams do not learn about it until they encounter it during their own submission review.
4. Why AI Bolted Onto Siloed Functions Does Not Solve the Problem
The most common pharma AI adoption pattern in 2025 and 2026 is individual function AI deployment: the HEOR team adopts an AI literature review tool, the CI team adopts an AI competitive intelligence tool, the regulatory team adopts an AI dossier authoring tool, and the market access team adopts an AI payer analytics tool. Each tool delivers productivity within its function. None of them addresses the cross-domain intelligence synthesis problem that creates the largest strategic costs.
McKinsey identified this pattern in their 2025 analysis of generative AI scaling in life sciences. A scalable AI platform, they noted, allows organisations to standardise infrastructure and data pipelines so each new use case builds on the previous one, reducing duplication across business units and allowing insights from one domain to be applied to another. The organisations that failed to capture AI's productivity potential were those that deployed AI tools within functions without building the cross-functional intelligence layer that connects them. McKinsey's broader estimate that gen AI could unlock $60 billion to $110 billion annually in pharmaceutical and medical-product companies reflects integrated, cross-domain AI deployment, not function-by-function point tool adoption.
Simply bolting AI onto business as usual, as McKinsey noted in their 2025 review of pharma AI implementation, will not deliver tangible results. The transformative value in pharma AI is in the cross-domain synthesis that multi-domain research enables, not in the incremental efficiency of faster single-domain queries.
5. How KnolAI Delivers Multi-Domain Research from a Unified Knowledge Layer
KnolAI, the research intelligence module of the Knolens platform, is built specifically to deliver multi domain research pharma capability from a single unified enterprise knowledge layer. The architecture makes multi-domain synthesis not just possible but structurally guaranteed: because all five intelligence domains draw from the same governed enterprise knowledge layer and knowledge graph, every query that touches multiple domains returns results with the same factual consistency and the same source attribution standard.
When a HEOR analyst asks KnolAI for the evidence architecture for a NICE STA in second-line NSCLC, the response draws from the clinical evidence domain for the trial data, the HTA precedent domain for the NICE appraisal history in the indication, the regulatory landscape domain for the FDA and EMA approval precedents, the competitive intelligence domain for the competitor pipeline and HTA outcomes, and the commercial domain for the payer coverage precedents. All five domains are queried in a single workflow. The response is a structured multi-domain intelligence synthesis with claim-level source attribution for every element drawn from every domain.
The result is not just faster evidence synthesis. It is a qualitatively different kind of intelligence: cross-domain connections that no single-domain tool can surface. The connection between a G-BA ZVT decision for a competitor product and the ITC methodology requirements that NICE will apply to the new submission, surfaced from the same knowledge layer in the same query, is exactly the intelligence that prevents the late comparator discovery failure. The connection between a competitor's HTA outcome and the evidence bar that the new submission will be measured against is what gives the HEOR team the strategic lead time to address vulnerabilities before they appear in a reviewer's clarification request.
6. Multi-Domain Research for Protocol Design: The Phase II Opportunity
The highest-value application of multi-domain research in pharma is not post-approval dossier preparation. It is Phase II protocol design, where intelligence from all five domains shapes the trial design decisions that determine the quality of the submission years later.
A Phase II protocol designed with multi-domain intelligence incorporates: the clinical endpoint evidence from analogous trials in the indication, the regulatory precedent for endpoint acceptance in the specific indication and mechanism class, the HTA body evidence requirements for endpoint patient-relevance across NICE, G-BA, and HAS, the competitive landscape of endpoints that competitor trials have used and the HTA outcomes those endpoint choices produced, and the payer evidence requirements for the specific indication and product class. This is not a checklist of five separate inputs assembled from five separate reports. It is a connected intelligence picture that the protocol design team can navigate as a single coherent view.
KnolAI generates this multi-domain protocol intelligence brief from the Knolens knowledge layer in a single structured output. Clinical and HEOR teams using KnolAI for Phase II protocol design consistently produce protocols with stronger HTA alignment, more defensible comparator choices, and more comprehensive subgroup pre-specification, because they are designing with the full intelligence landscape visible, not from the clinical domain alone.
7. The Compounding Value of Multi-Domain Intelligence Over Time
One of the most significant and least appreciated properties of a multi-domain research platform is that its value compounds over time. Every piece of intelligence generated from the platform, every clinical evidence synthesis, every HTA precedent analysis, every competitive intelligence brief, adds to the shared knowledge layer that all future queries draw from.
In a siloed research model, the intelligence generated by the HEOR team for a submission sits in a static dossier document. It cannot be queried by the CI team. It cannot be updated when new evidence emerges without a full document revision exercise. It cannot inform the Phase II protocol design for the next product in the portfolio without someone manually reading and summarising the previous dossier. The knowledge depreciates in value from the day the document is filed.
In the KnolAI multi-domain model, the intelligence generated for a NICE STA in second-line NSCLC adds validated entity-relationship triples to the Knolens knowledge graph. Those triples are available to every subsequent query for the same indication: the Phase II protocol design brief for the next NSCLC product, the competitor monitoring programme for the indication, the market access scenario plan for the next HTA cycle. The knowledge compounds in value with every query, every source added, and every validated triple stored. Accenture's analysis of cross-domain AI transformation in pharma found that organisations that build unified intelligence platforms rather than siloed function tools achieve this compounding value effect, fundamentally rewriting the R&D productivity equation.
8. Practical Multi-Domain Research Workflows in Pharma
Four specific pharma research workflows illustrate the practical value of multi-domain research capability over single-domain tools.
Workflow 1, Simultaneous multi-HTA submission preparation: NICE, G-BA, HAS, and ICER each require different evidence framings of the same clinical data. A multi-domain research platform generates all four body-specific evidence summaries from the same knowledge layer simultaneously, with cross-submission consistency guaranteed because all four draw from the same source facts.
Workflow 2, Integrated IRA negotiation preparation: CMS MFP determination draws on Medicare expenditure data, therapeutic alternative landscape, and clinical differentiation evidence. These are three separate intelligence domains. Multi-domain research connects them in a single structured intelligence brief that gives the market access team the full picture they need for negotiation preparation.
Workflow 3, Cross-portfolio competitive scenario planning: A portfolio review requires competitive intelligence from the pipeline domain, clinical evidence from the trial data domain, regulatory outcomes from the approval precedent domain, and HTA outcomes from the payer decision domain for every competing product across the relevant therapeutic areas. Multi-domain research makes this a single query workflow. Single-domain tools make it a multi-week synthesis exercise.
Workflow 4, Phase III readout impact assessment: When a competitor achieves a Phase III readout, the impact on your portfolio requires simultaneous analysis across the HTA comparator domain, the regulatory evidence bar domain, the competitive positioning domain, and the clinical protocol domain. Multi-domain research generates this integrated impact assessment in hours. Siloed research generates four separate analyses that must be manually reconciled before any strategic response is possible.
9. How Fast Can Your Team Deploy Multi-Domain Research with KnolAI?
Deploying KnolAI as your organisation's multi domain research pharma capability does not require rebuilding your existing research infrastructure. KnolAI integrates into existing HEOR, CI, and regulatory workflows as the unified intelligence layer that connects what was previously separate. Most pharma teams are running their first cross-domain intelligence queries within two weeks of onboarding.
Sprint 1, Weeks 1 to 2, Unified knowledge layer active across all five domains: KnolAI is connected to your indication scope. The Knolens knowledge layer is populated with validated clinical, regulatory, HTA, competitive, and commercial intelligence for your therapeutic area. Your team runs the first multi-domain queries and receives responses that draw from all five domains simultaneously. The difference from a single-domain literature tool is visible in the first session.
Sprint 2, Weeks 3 to 4, Multi-domain workflows configured for your primary use cases: Protocol design intelligence briefs, multi-HTA evidence summaries, and competitive landscape assessments are configured for your indication and target bodies. Agentic monitoring workflows are activated to continuously update the knowledge layer as new evidence, regulatory decisions, and competitive signals emerge across all five domains.
Sprint 3, Weeks 5 to 6, Cross-functional distribution and governance live: The Knolens knowledge layer is shared across HEOR, CI, regulatory, and market access functions. Each function queries the same governed knowledge base and receives domain-relevant outputs in their required formats. The institutional knowledge compound begins: every query adds validated intelligence to the shared layer that all future queries can draw on.[4]
Conclusion
The pharma productivity crisis documented by Eroom's Law, the declining return on $300 billion of annual R&D investment, has many causes. Organisational research silos are one of the most addressable. When the intelligence domains that should be informing the same strategic decisions are separated by function, tool, and workflow, the cost shows up in late comparator discoveries, inconsistent cross-submission claims, missed competitive signals, and delayed launches. None of these failures is inevitable. All of them are preventable with the right intelligence architecture.
At Pienomial, we built KnolAI as the multi domain research pharma platform that connects clinical, regulatory, HTA, competitive, and commercial intelligence in a single governed knowledge layer, because we believe that the value of AI in pharma is not in making each function slightly faster. It is in making the connections across functions that no single-domain tool can make, and delivering the integrated intelligence that the most consequential pharma decisions actually require.
CTA: See how KnolAI delivers multi-domain research intelligence. Book a demo with the Pienomial team.
