Ahead of The MedTech Forum 2026 in Stockholm, Vamstar explores why the next phase of MedTech growth will depend not only on innovation, but on the ability to execute across tenders, pricing, market access, contracts and evidence management.

In Stockholm, the agenda will be innovation. In every boardroom, the question is execution.

When delegates take their seats at The MedTech Forum 2026 in Stockholm from 11–13 May, the official programme will read the way MedTech programmes have read for years: innovation, digital health, AI, regulation, access to care, sustainability. These are the right themes. They reflect where the sector is moving and where the pressure is building. MedTech Europe convenes the Forum as Europe’s largest health and medical technology industry conference, drawing executives, policymakers, healthcare professionals and investors from across the continent.

But beneath the headline themes, a sharper question has taken hold of the industry. It is the question most likely to decide who leaves Stockholm with momentum, and who leaves with another set of unfinished initiatives.

How do MedTech companies actually execute in this environment?

Because the pressure on the sector is no longer isolated to one function. It is no longer just a regulatory challenge, a pricing challenge, a procurement challenge or an innovation challenge. It is all of them, at once.

MedTech companies are being asked to innovate faster, prove value earlier, comply with more demanding regulatory and data requirements, respond to more sophisticated procurement criteria, absorb cost volatility, manage sustainability expectations, and protect margins in markets that are becoming harder to navigate.

The industry does not lack ambition. It lacks operational bandwidth.

That is why the most consequential conversations in Stockholm may not be about what comes next for MedTech innovation. They will be about how MedTech organisations build the commercial infrastructure to act on it.

AI is moving from experiment to execution

Artificial intelligence will inevitably dominate the agenda at The MedTech Forum 2026. Across the industry, AI is moving beyond the laboratory, the clinical setting and the innovation showcase. It is becoming part of how companies plan, price, bid, contract, evidence and commercialise.

The recent industry commentary reflects that shift. IQVIA’s 2026 MedTech outlook points to AI, real-world evidence, medical device regulation, diagnostics, robotics and digital health as defining forces for the year ahead. ZS has gone further, framing 2026 as an inflection point where agentic AI begins moving “from the lab to the line” — executing across core MedTech value streams rather than supporting isolated tasks.

That distinction matters more than most leaders are willing to admit out loud.

For years, AI conversations in MedTech have focused on invention: better diagnostics, smarter devices, improved imaging, accelerated R&D, more personalised care. These remain critical. But the next phase of AI adoption will be judged by something less glamorous and more consequential — whether it can help MedTech businesses operate better.

Can AI help commercial teams identify the right tender opportunities earlier? Can it qualify complex RFPs against product, regulatory, pricing and evidence requirements? Can it help pricing teams see where margin is leaking? Can it surface contractual escalation rights before the window to act closes? Can it structure evidence for value-based procurement, sustainability scoring and market access? Can it connect workflows that today live across email inboxes, spreadsheets, portals, local teams and disconnected systems?

This is where the AI conversation becomes practical, and where many MedTech AI strategies quietly run aground.

The question is no longer whether AI has potential in MedTech. The question is whether companies can embed AI into the commercial operating model with enough governance, context and domain understanding to make it useful at the moments that matter.

Access has become an evidence problem

Access to care is one of the Forum’s stated themes — and rightly so. But for MedTech suppliers, access is increasingly tied to a more specific commercial discipline: producing the right evidence at the right moment.

The old commercial playbook was built around product performance, clinical differentiation and relationship-led selling. Those still matter. But buyers are now asking broader questions, and they are asking them on tighter timelines.

What is the total cost of ownership? How does this device support pathway efficiency? What outcomes evidence supports the claim? How does the solution perform across different care settings? What sustainability evidence can you provide? What cybersecurity, data governance and resilience assurances are in place? What post-award reporting can you support?

This is the direction of travel in procurement and market access. Value-based procurement is becoming more visible, more structured and more consequential. In the UK, the Department of Health and Social Care has been advancing value-based procurement guidance for MedTech, with NHS trusts piloting the approach and wider rollout expected. Healthcare procurement commentary for 2026 increasingly points to sustainability and value-based procurement as the lens through which lifecycle costs, maintenance, end-of-life impact and broader value considerations will shape sourcing decisions.

This creates a new burden — and a new test — for commercial teams.

It is no longer enough to have evidence somewhere in the organisation. It has to be findable, structured, current, localised and connected to the claims being made in bids, dossiers and buyer conversations.

For most companies, that is still a meaningful gap.

Evidence sits across fragmented systems, old submissions, PDFs, market access materials, clinical repositories, sustainability reports, regulatory documents and local-market files. The result is duplication, inconsistency and slow response cycles — at exactly the moment when procurement timelines are tightening, buyer requirements are expanding, and internal review cycles are growing more complex.

This is why evidence management is no longer an administrative task. It is a commercial capability — and increasingly, a competitive one.

Regulation, cybersecurity and data governance have become trust factors

Regulatory development is another core theme for MedTech Forum 2026. But the commercial implications are often understated.

Regulation no longer sits neatly at the end of the process as a compliance checkpoint. It increasingly shapes market access, buyer confidence, procurement eligibility and commercial speed. Industry commentary for 2026 points to regulatory convergence and change, particularly around software, AI-enabled medical devices, quality systems and data foundations. Cybersecurity is following the same trajectory. For connected devices, digital health solutions and software-enabled products, cybersecurity has moved from a technical control to part of the trust architecture buyers expect to see before adoption is even on the table.

The same is true for AI governance.

As AI moves into commercial workflows, MedTech companies will need to demonstrate that outputs are explainable, evidence-linked and controlled. A generated tender response, pricing recommendation, market access summary or contract interpretation cannot be a black box. It has to be traceable to source evidence, reviewed by accountable teams, and governed through a process that withstands procurement and regulatory scrutiny.

This is where many AI initiatives stall.

The technology may work in a demo. It may produce impressive outputs in a controlled setting. But once it enters a regulated commercial environment, the requirements change entirely. The organisation needs permissions, auditability, human-in-the-loop review, workflow integration, data controls and domain-specific understanding.

That is why MedTech AI implementation cannot be treated as a generic software deployment. It has to be engineered around the way the business actually works — and around the regulatory weather it actually operates in.

Sustainability is moving into commercial scoring

Sustainability will also feature prominently in Stockholm. But the commercial shift is more specific than broad ESG ambition.

Sustainability is moving into procurement. It is becoming part of supplier evaluation, tender scoring, lifecycle assessment and buyer risk management. That changes the nature of the work.

MedTech companies need more than sustainability statements. They need procurement-grade evidence: product-level information, lifecycle data, supply chain documentation, packaging details, service models, waste reduction claims, carbon impact, and proof of alignment with each buyer’s specific requirements.

For global organisations, this becomes more complex still. Sustainability expectations vary by market, buyer type and procurement framework. What is sufficient for one tender may be inadequate for another. What is useful at corporate level may not translate into a buyer-ready evidence pack.

The companies that perform in this environment will be those that turn sustainability from a reporting function into a commercial asset — connecting sustainability evidence to tender requirements, product claims, market access narratives and post-award commitments, and ensuring teams can reuse validated evidence rather than recreating it under deadline pressure.

Margin pressure is rewriting the case for commercial intelligence

The MedTech sector is operating in a more volatile cost environment than it has in a decade. Supply chain disruption, inflation, energy costs, logistics pressure, tariff exposure and geopolitical uncertainty are all making margin protection harder — and the levers to defend margin less obvious.

This is where commercial intelligence becomes critical.

Many MedTech organisations already hold contractual mechanisms that could support margin recovery: price escalation clauses, renegotiation triggers, indexation rights, volume commitments, service-level provisions, change-of-law clauses. But those rights are buried across thousands of agreements, tender documents, framework contracts and local amendments.

Knowing those rights exist is not the same as being able to act on them.

By the time teams manually search for the relevant clause, validate the trigger condition, gather supporting evidence and prepare a buyer engagement pack, the commercial window has often already narrowed — sometimes closed.

This is one of the clearest cases for moving AI into the contract stack.

The opportunity is not simply to store contracts more efficiently. It is to read them, classify them, monitor them, and connect them to commercial action. Which agreements contain escalation rights? Which markets are exposed? Which clauses are active? Which buyer conversations need to happen now? What evidence is required to support the request?

In a margin-constrained environment, speed to insight becomes speed to recovery. The companies that find that speed first will compound the advantage quietly, contract by contract, quarter by quarter.

The next MedTech operating model will be built around connected commercial execution

Taken together, the major themes around The MedTech Forum 2026 point to a single conclusion that the official agenda only hints at.

The next MedTech operating model will not be built around isolated digital projects. It will be built around connected commercial execution.

That means pricing, tendering, contracting, market access, evidence management, regulatory intelligence and sustainability proof can no longer operate as disconnected workstreams. They are increasingly part of the same commercial system.

A tender response may now require pricing discipline, product matching, regulatory eligibility, sustainability evidence, value claims, cybersecurity documentation and local market intelligence — drawn together inside a window measured in days.

A market access strategy may require policy monitoring, evidence mapping, buyer segmentation, reimbursement intelligence and competitor activity — refreshed continuously, not once a quarter.

A pricing decision may depend on tender history, contract obligations, local market dynamics, competitor benchmarks and margin exposure — all visible at the same moment, to the same team.

A contract review may surface rights that directly affect revenue protection — but only if the right clauses can be found in time.

This is where AI creates real value. Not as a layer of automation hovering above the business, but as an operating layer embedded inside the workflows that determine commercial performance.

At Vamstar, this is the problem we are focused on.

Our work with MedTech, pharmaceutical and life sciences organisations is centred on helping commercial teams turn fragmented data, documents and market signals into structured action. Through Polaris AI, Vamstar supports organisations across tender and RFP management, pricing intelligence, contract visibility, value and market access evidence, and forward-deployed engineering for the complex operational environments that define this industry.

The goal is not to replace expert teams. It is to give them the intelligence, workflow control and evidence infrastructure they need to move faster, with greater confidence, against opponents and timelines that are not slowing down.

What we expect to discuss in Stockholm

As Vamstar’s team joins industry leaders at The MedTech Forum 2026, we expect the most valuable conversations to sit at the intersection of innovation and execution:

How do MedTech companies move AI from pilots into governed workflows? How do commercial teams respond to more complex tender and procurement requirements? How can pricing, contracting and evidence functions operate from a shared intelligence layer? How do organisations prepare for value-based procurement at scale? How can sustainability, cybersecurity and regulatory evidence be made commercially usable, not just compliant? How do companies protect margin when commercial rights are hidden across fragmented contract portfolios?

These are not abstract questions. They are already shaping how MedTech companies compete — and they will shape who wins the next five years of the industry.

The organisations that succeed in the next phase of MedTech will not simply be those with the strongest products or the most ambitious innovation strategies. They will be the companies able to convert complexity into execution.

That means knowing where the opportunity is. Knowing what the buyer is asking for. Knowing what evidence supports the claim. Knowing which price corridors are defensible. Knowing which contracts contain commercial rights. Knowing which workflows need human judgement and which can be accelerated through AI.

The future of MedTech will still be shaped by innovation. But the winners will be the companies that can operationalise it.

That is the conversation we are looking forward to having in Stockholm.

For decades, competition in healthcare contracting has been defined by one thing: price. Lowest price won tenders, shaped budgets, and guided procurement teams across Europe.

That reality is changing fast.

Across the European Union and the United Kingdom, the concept of “most economically advantageous tender” (MEAT) or “most advantageous tender” (MAT) has become the new legal default. These frameworks compel contracting authorities to evaluate bids based on quality, sustainability, and life-cycle value, not simply the cheapest offer.

This shift marks a structural, policy-driven invitation to monetise non-price strengths. The manufacturers and suppliers who can evidence clinical performance, resilience, digital integration, and environmental responsibility will increasingly outperform those competing on unit cost alone.

But identifying, quantifying, and anticipating how these non-price criteria (NPC) are scored requires a new level of intelligence. It demands that manufacturers and market access teams move beyond traditional bid-desk reactivity toward data-driven understanding of how buyers think and what they reward.

The Policy Inflection Point

A Pan-European Recalibration

The movement toward non-price evaluation is not an abstract ideal; it is codified in law. The European Union’s procurement directives, transposed across member states, require contracting authorities to consider social, environmental, and innovation factors in award decisions.

New digital eForms and structured publication standards under the TED data model now capture award criteria, weights, and even sub-criteria in machine-readable form. This creates unprecedented transparency into how contracting bodies assign value across categories and over time.

The UK’s Parallel Evolution

The UK’s Procurement Act 2023 represents one of the most radical domestic overhauls of public procurement in decades. It mandates explicit disclosure of how criteria are scored, requires publication of assessment summaries, and codifies sustainability weightings across NHS tenders.

Within the NHS, a minimum 10% weighting for Net Zero and Social Value now applies to every procurement. Carbon-reduction plans, Evergreen assessments, and sustainability reporting are no longer optional. They are built into the tender architecture itself.

Together, these frameworks establish a common European pattern: the value of non-price performance is measurable, visible, and monetisable.

The New Currency: Data

Structured Publication and Richer Fields

Modern eForms have transformed how procurement information is disclosed. Each notice now encodes detailed fields such as criteria, weights, lots, award values, tender counts, and sub-criteria explanations. The TED data model and UK equivalents capture information that once existed only in static PDFs.

For suppliers, this structured data is a goldmine. It enables systematic tracking of how often certain criteria appear, which buyers assign weight to quality versus sustainability, and how these preferences evolve by product category or geography.

When combined across thousands of tenders, the resulting dataset reveals a live map of market expectations, not just what buyers buy, but what they value.

Policy-Driven Weightings

Different jurisdictions shape the landscape with their own policy signals.

• NHS England’s Net Zero & Social Value weighting embeds environmental and community considerations into procurement.
• France’s Code de la Commande Publique requires life-cycle cost considerations and social inclusion metrics.
• Germany’s Vergaberecht increasingly supports innovation and environmental criteria in healthcare procurement.

The outcome is consistent: buyers are compelled to quantify non-price dimensions. Suppliers that can match evidence to these weightings gain a measurable advantage.

Your cursor blink on a 96-page tender.

The document hums on your screen, dense, precise, and merciless. Somewhere in the annexes, between clauses on sterilisation, shelf life, and post-market surveillance, sits the one requirement that could derail your entire submission if you miss it.

You scroll again.

Another table.

Another column.

Another round of matching.

Product codes to catalogue numbers. Certificates to standards. ISO references to country-specific equivalents.

You tell yourself it’s almost done, but you know matching never really ends.

Matching Is Miserable

Anyone who has ever managed bids or contracts in MedTech or Pharma knows this feeling.

Matching is the most vital and least celebrated part of commercial operations. It’s where compliance hides and where errors quietly multiply. You can’t automate your way around it. Every tender demands cross-checking SKUs, device classifications, sterilisation methods, and conformity evidence against the buyer’s language. One mismatch, a misplaced clause, a missing declaration, an expired certificate, can wipe out weeks of work.

The problem isn’t intelligence. It’s endurance.

Humans lose focus. Screens blur. Standards change.

And that’s why matching feels like punishment.

The Moment Tender AI Takes Over

Now imagine the same process without the pain.

Tender AI doesn’t blink. It doesn’t sigh. It doesn’t care how many appendices the RFx/ Tender has or how inconsistent the terminology is. It reads every document, every regulation, every product file and connects meaning where humans see noise.

When you upload a Tender document, Tender AI extracts structure, intent, and relationships. It understands that when a buyer requests “validation of an EtO sterilisation process,” it points to EN ISO 11135, the internationally recognised standard describing how that validation must be conducted.

It recognises that “UDI traceability” connects to the EU MDR Article 27 and Annex VI. It links “biocompatibility per ISO 10993” even when the supplier document phrases it as “cytotoxicity and sensitisation testing per part 5 and 10.”

It doesn’t confuse these terms, it contextualises them.

That’s what makes it intelligent.

Matching Isn’t Admin, It’s Survival

In MedTech, Pharma, Biotech, and every other division within lifesciences for that matter, the wrong match doesn’t just cost points on a scoring sheet, it can cost market access.

A missing reference to the correct sterilisation standard can delay approvals.

A mis-aligned post-market clause can trigger an audit.

A misunderstood evidence requirement can stall reimbursement.

Matching is not bureaucracy. It’s the bloodstream of compliance.

Yet every day, commercial and regulatory teams drown in spreadsheets and PDFs manually aligning content across geographies, languages, and procurement portals.

Tender AI doesn’t just save time. It eliminates doubt.

It builds traceability, consistency, and confidence into a process that has historically relied on human stamina.

What Really Changes

When matching moves from manual to intelligent, the rhythm of work itself changes. The constant firefighting gives way to foresight. Instead of reacting to every new document, every conflicting clause, teams begin to see the bigger picture, the patterns, the risks, the opportunities.

The process stops being about survival and becomes about strategy. You’re no longer buried in the details of which certificate aligns with which annex; you’re thinking about market positioning, value communication, and differentiation. Tender AI takes care of the structure so you can focus on the story.

It learns from every submission, every correction, every win or loss, building a living memory that strengthens the next bid. What once drained attention now builds momentum. Matching no longer consumes your expertise, it multiplies it.

The Human-AI Divide

There’s an irony at the heart of this transformation. Matching is the kind of work that exhausts people precisely because it demands a kind of perfection that humans aren’t built for. It requires total focus, infinite patience, and no margin for error, qualities that machines excel at but that wear down even the best teams over time. Artificial intelligence doesn’t see matching as punishment; it sees it as a simple task.

It thrives in the repetition, the structure, and the quiet logic of connecting one clause to another, one document to the next. And that’s where the divide truly lies: humans bring judgment, creativity, and strategy, AI brings precision, endurance, and scale. Together, they form a new kind of intelligence that makes the impossible suddenly routine.

The Quiet Revolution

You’re still watching the cursor blink, but something feels different. The tension has gone. The Tender submission that once looked like a wall of impossible text now feels ordered, almost logical. The noise has turned into structure. Every clause is aligned, every requirement accounted for, every standard cross-referenced without the endless late-night checking. Tender AI has done the quiet work, the kind that doesn’t make headlines but changes everything.

It has turned the chaos of matching into an invisible layer of order that powers your next submission, your next deal, your next strategy. There’s no fanfare, no drama, just precision doing its job in the background. And maybe that’s the most radical change of all, the revolution that happens when technology stops shouting and simply starts delivering.

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.