In today’s high-stakes health-sector procurement landscape, speed and precision are no longer optional—they are commercial imperatives. Yet a persistent drain on time, resources, and revenue often goes unnoticed: the relentless influx of unstructured Request for Quotations (RFQs) inundating commercial teams daily.

Mid-tier and low-value RFQs—often received via email, PDF, or fragmented digital portals—drain bandwidth and deliver limited returns. The issue extends beyond volume; the real challenge lies in identifying relevance, improving responsiveness, and optimising resource allocation. Many organisations remain tethered to outdated workflows: manual copy-pasting, laborious SKU matching, and drawn-out communication loops—all contributing to missed deadlines and eroded margins.

This operational drag continues to slow execution and dilute strategic focus. But AI is now transforming this process—decisively.

Unmasking the Strategic Blind Spot

Pharma, MedTech, and Biotech companies have invested heavily in platforms optimised for large-scale tenders. Yet these systems often fall short when handling the high-frequency RFQs issued by decentralised hospitals, regional buying groups, and local health systems. As a result, commercial teams are forced to spend disproportionate time on basic intake and filtering—diverting attention from response quality and margin optimisation.

This is the critical blind spot. The cumulative impact of numerous smaller, overlooked RFQs can equal—or even exceed—the commercial value of a major tender.

Without automation, teams are compelled to prioritise selectively, often leaving potential revenue and market responsiveness untapped.

AI-Powered Transformation: Beyond Simple Automation

The next evolution in procurement transcends digitisation. It is intelligent automation. Healthcare-trained AI now delivers powerful capabilities to:

• Extract and structure RFQ content from diverse formats with high precision

• Filter out irrelevant requests, spotlighting commercially viable opportunities

• Match items to internal SKUs using advanced, ontology-driven logic

This is the core of Vamstar’s RFQ Fasttrack Engine—a modular, out-of-the-box solution for healthcare supply chains, requiring no complex integration.

What this looks like in action:

• AI-powered Triage: Automated parsing of RFQs from diverse sources (emails, PDFs, documents), extracting critical fields, such as deadlines, lots, SKUs, and quantities with >90% accuracy.

• Strategic Filtering: Sector-specific large language models (LLMs) assess RFQs for commercial relevance, ensuring teams focus only on high-value prospects.

• Instant SKU Mapping: Seamless mapping of requested items to internal product families and catalogues—eliminating time-consuming manual reconciliation.

• Margin Visibility: Built-in calculators use pricing history and win-loss data to validate profitability before bids are submitted.

• Scalable Workflows: A flexible framework that allows organisations to begin with triage and expand to catalogue mapping, margin analysis, or full response workflows—fully compatible with CRM systems like Salesforce.

Turning Cost Centres into Profit Catalysts

Early adopters are already seeing a 2–3x increase in mid-tier RFQ response rates and significant reductions in manual triage time. More importantly, AI is reshaping how RFQs are perceived—from operational clutter to a stream of qualified commercial opportunities.

Intelligent automation enables lean tendering teams to scale output, enhance bid quality, and improve commercial precision—without additional headcount.

Modular AI That Fits Your Pace

One of the most significant advantages of modular AI deployment is flexibility. You don’t need to rip and replace your current systems. With Vamstar, teams can start with AI-powered triage and expand into catalogue mapping, margin validation, and full workflow automation based on their needs and maturity.

This isn’t about short-term efficiency gains. It’s about a long-term shift to data-driven, scalable commercial excellence.

Looking Ahead: Agentic Commercial Operations

The next frontier is agentic workflows—where AI not only supports decision-making but autonomously executes actions within defined parameters. In RFQ management, this means AI that:

• Recommends pursuit strategies

• Suggests pricing based on historical success

• Compiles and submits quotes—fully auditable and compliant

This capability is no longer conceptual—it is fast becoming reality. And organisations that move early will be best positioned to lead this transition, both strategically and operationally.

Final Thought: Unlock the Hidden Value in Your Inbox

The influx of RFQs may seem like routine administrative noise, but it holds substantial commercial value. With the right AI infrastructure, what was once unstructured chaos becomes a repeatable, high-yield growth engine.

The RFQ challenge isn’t going away. But the ability to respond faster, smarter, and with greater margin clarity is now within reach—turning inefficiency into strategic advantage.

Technology executives in leading medtech and pharmaceutical enterprises are under relentless pressure to modernise procurement workflows, enforce multi-jurisdictional compliance, and scale operations globally—all while protecting patient safety and proprietary IP. Yet many organisations remain burdened by siloed repositories, outdated ECM platforms, and manual spreadsheet-driven processes that stifle agility. For those tasked with implementing transformative solutions, embracing AI-powered tender data curation isn’t simply a nice-to-have—it’s a mission-critical strategy to break down silos, accelerate response times, and secure a competitive edge.

The Enterprise Pain Points: Why Traditional Approaches Fail

• Legacy System Fragmentation

ERP, PLM, GxP archives, procurement portals, and bespoke point solutions rarely “talk” to each other. This lack of interoperability forces high-value teams to manually reconcile metadata, document revisions, and version histories.

• Regulatory & Security Overhead

Maintaining audit trails across multi-jurisdictional tenders—often in 10+ languages—requires rigorous controls, encryption-at-rest, and role-based access, yet these are nearly impossible to enforce consistently with spreadsheet-driven processes.

• High-Touch, Low-Value Effort

Enterprise technologists spend up to 30% of their time troubleshooting data integrity issues, delaying AI/ML pilots and advanced analytics initiatives that could otherwise optimise supply-chain resilience and drive down COGS.

The landscape of AI-driven business transformation is dominated by companies that often operate behind the scenes, focusing on specialised verticals rather than broad, consumer-facing applications.

These companies utilise vertical AI to tailor solutions specifically for individual sectors, maximising efficiency and strategic insight.

1. Professional Services: Vertical AI is making significant strides in professional services.

For instance, legal tech companies like Harvey and Responsiv leverage AI to expedite legal research and documentation processes, allowing professionals to focus more on strategic aspects of their work.

2. Business Services: Vamstar is enhancing efficiencies in the life sciences sector by optimising procurement processes and improving data analytics for better decision-making in lifescience organisations.

3. Financial Services: In the financial sector, companies like Truewind and Trullion are transforming financial management with AI, automating workflows to ensure precision and efficiency.

These tools not only streamline operations but also provide predictive analytics that can transform underwriting and customer services.

4. Healthcare and Biotech: AI’s role in healthcare is profound, with companies developing solutions that streamline drug development and enhance precision medicine.

For example, Deloitte highlights how AI can optimise clinical trials and advance manufacturing processes in the biopharma sector, potentially speeding up the delivery of new therapies.

5. Retail and E-commerce: Retail giants like Toast and Shopify have successfully integrated AI into their operations, transforming how businesses manage inventory, process payments, and interact with customers.

Their platforms demonstrate how vertical-specific AI can not only support existing business operations but also create new opportunities for growth and customer engagement.

Vertical AI companies, such as Vamstar in the life sciences sector, illustrate the subtlety with which AI can be integrated into industry-specific workflows, enhancing efficiency and profitability without the broad public visibility of consumer-facing AI technologies.

As these specialised AI solutions continue to evolve, they promise to transform industries by addressing specific challenges and maximising operational efficiencies.

Introduction

The European Federation of Pharmaceutical Industries and Associations (EFPIA) has recently published a white paper addressing key challenges in medicine procurement within the EU and UK. This white paper emphasises the need for effective and sustainable procurement practices that can deliver high-quality medicines to patients in appropriate quantities and at the right time.

Background on Tendering Practices

Tendering processes in pharmaceuticals play a crucial role in determining how medicines are procured and distributed. These processes, however, have been plagued by various issues that affect competition and limit access to essential medications. A recent EFPIA survey highlighted seven anomalies in national tendering practices in the EU & UK that harm competition and potentially limit patients’ access to medicines.

EFPIA’s White Paper Overview

EFPIA’s white paper, published on February 10, 2022, presents a sector-specific contribution to the broader debate on the efficacy of the EU procurement rules. It aims to improve not only the formal public procurement procedures but also the informal tendering processes increasingly employed outside the confines of Directive 2014/24/EU.

The Ten Recommendations

1.Transparency in Procurement Processes: Advocates for clearer and more open procedures to enhance fairness.
2. Balancing Cost with Quality: Proposes evaluating tenders based on both price and quality.
3. Long-Term Agreements: Suggests longer contracts to ensure stable supply and predictability for manufacturers.
4. Risk Mitigation Strategies: Recommends measures to prevent drug shortages, including contingency plans.
5. Incentivising Innovation: Encourages mechanisms to reward and promote the development of new treatments.
6. Sustainable Pricing Models: Calls for pricing that reflects the value of medicines while ensuring accessibility.
7. Collaboration with Stakeholders: Stresses the importance of involving all relevant parties in the procurement process.
8. Flexibility in Contracts: Advises adapting contracts as new treatments become available or needs change.
9. Streamlined Administrative Processes: Recommends reducing bureaucracy in the procurement process.
10. Regular Monitoring and Review: Urges ongoing assessment of procurement practices for effectiveness.

Expanded Insights

EFPIA’s recommendations focus on three distinct types of public procurement rules and practices. These include traditional EU public procurement rules, the 2014 Joint Procurement Agreement, and informal tendering processes. The recommendations seek to enhance efficiency, bolster fair competition, and address flaws revealed in market feedback obtained from a survey of tendering practices in 18 countries across the EU and the UK.

Impact on Healthcare and Industry

Implementing these recommendations could lead to more affordable medication prices, fostering accessibility and stimulating growth in the pharmaceutical sector. Furthermore, encouraging innovation could result in the development of new, more effective treatments, significantly improving patient outcomes.

Conclusion

EFPIA’s recommendations offer a roadmap for enhancing medicine procurement practices in the EU and UK, aiming to improve access to quality medicines and foster a more dynamic pharmaceutical sector. While challenges in implementation exist, the potential benefits for healthcare delivery and industry growth are substantial.

In this context, platforms like Vamstar’s could play a crucial role. Vamstar, with its advanced digital procurement solutions, can support the adoption of these recommendations by providing a more efficient, transparent, and data-driven approach to procurement. Its technology could facilitate better collaboration among stakeholders, streamline the procurement process, and offer insights into market trends and supplier performance.

This aligns perfectly with EFPIA’s call for improved procedures and increased transparency in the tendering process. By leveraging such innovative platforms, stakeholders can work together more effectively to ensure a healthcare system that not only meets the needs of its patients but also aligns with the resilience and growth goals of the EU.

Background:

The House of Commons Committee on Public Accounts released a report on March 20th, 2024, addressing the inefficiencies in the NHS Supply Chain procurement processes.

The report shed light on various challenges, including difficulties in achieving market share targets, oversight problems, lack of trust among stakeholders, and delayed transformation initiatives.

In this article, we explore the context of the report and propose recommendations for implementing an AI-based data orchestration platform to foster consistency, trust, and transparency in the procurement processes.

Introduction:

The NHS spends approximately £8 billion annually on medical equipment and consumables. NHS Supply Chain, established in 2018, aims to deliver savings and increase market share by aggregating spending power and reducing price variations.

However, challenges persist in persuading trusts to utilise the NHS Supply Chain, resulting in missed savings opportunities. This paper explores how AI-based data orchestration systems can address these challenges and improve NHS Supply Chain’s efficiency and patient outcomes.

Key Challenges:

1. Low Trust Participation: NHS Supply Chain has failed to persuade trusts to use its services, resulting in only 57% market share against a target of 80% by 2023-24. This limits potential savings and efficiency gains.
2. Weak Oversight and Support: NHSE has been weak in its oversight and support of NHS Supply Chain, failing to validate claimed savings and provide adequate financial support for modernization efforts.
3. Lack of Trust Accountability: NHSE does not effectively challenge trusts to purchase more through the NHS Supply Chain, relying on trusts to analyse procurement data and change practices independently.
4. Inconsistent Savings Reporting: NHS Supply Chain has used multiple methods to calculate and report savings, causing confusion and mistrust among trusts.
5. Delayed Transformation Benefits: NHS Supply Chain’s transformation program, aimed at improving its business, is expected to run from 2022-30. Benefits will take several years to materialise due to capacity constraints and legacy system challenges.
6. Balancing Cost and Quality: There are concerns that a focus on costs may impact product quality and patient outcomes. Clinicians need to be more involved in purchasing choices to ensure patient care is considered alongside value and cost.

To address these challenges and enhance the efficiency of the NHS Supply Chain, we propose the implementation of an AI-driven data orchestration technology and analytics to reduce risk to the NHS. This technology will ensure data consistency, build trust, and promote transparency in the procurement processes.

AI-based Data Orchestration Solution will support:

1. Predictive Demand Forecasting: Implement AI algorithms to analyse historical procurement data, patient demographics, and clinical trends to accurately forecast demand for medical equipment and consumables. This will enable NHS Supply Chain to optimise inventory levels, reduce stockouts, and improve trust participation by ensuring product availability.
2. Dynamic Pricing Optimisation: Develop an AI-powered pricing engine that continuously analyses market conditions, supplier contracts, and trust purchasing patterns to offer competitive and transparent prices. This builds trust and confidence, and encourages increased utilisation of the NHS Supply Chain.
3. Intelligent Procurement Analytics: Deploy AI-driven analytics to identify purchasing patterns, price variations, and potential savings opportunities across trusts. Provide actionable insights to NHSE and trusts, enabling data-driven challenges and accountability for utilising NHS Supply Chain.
4. Unified Savings Reporting: Establish a standardised, AI-powered savings calculation methodology that integrates data from NHS Supply Chain, trusts, and suppliers. Ensure consistency, transparency, and trust in reported savings across all stakeholders.
5. AI-assisted Transformation Management: Leverage AI project management tools to optimise resource allocation, identify critical paths, and monitor progress of NHS Supply Chain’s transformation program. Use predictive analytics to anticipate and mitigate risks, ensuring timely delivery of modernisation benefits.
6. Value-based Procurement: Implement an AI framework that incorporates clinical outcomes, patient satisfaction, and long-term cost savings into procurement decisions. Engage clinicians in defining value metrics and utilise AI to analyse real-world evidence, ensuring a balance between cost and quality.

Conclusion:

Implementing AI-based data orchestration systems can significantly enhance NHS Supply Chain’s efficiency, savings, and patient outcomes. By leveraging predictive analytics, dynamic pricing, intelligent procurement insights, unified savings reporting, AI-assisted transformation management, and value-based procurement, NHS Supply Chain can overcome existing challenges and drive trust participation. Collaboration among NHS Supply Chain, NHSE, trusts, and clinicians is crucial to realise the full potential of these AI solutions and ensure a sustainable, patient-centric procurement process.