The state of AI in tender management: 2025 report

AI in tender management is no longer a future scenario. It's an active market with measurable adoption, clear demand signals, and real performance data from organizations that have already made the shift.

This report is based on FBK's Life Sciences and Healthcare market signals — what we're seeing from client conversations, platform usage data, and the broader competitive landscape. It's not a survey of intentions. It's a snapshot of what's actually happening.

Two numbers tell the story of where the market stands today.

Requests for automated tender document analysis have grown 5x over the past year. This isn't gradual adoption — it's a sharp inflection point. Organizations that were exploring AI capabilities 12 months ago are now actively procuring them.

At the same time, 80% of RFPs and RFIs we see in the tender management space now explicitly require AI-powered capabilities. Buyers aren't asking whether a platform uses AI. They're specifying which AI capabilities they expect — document analysis, opportunity scoring, compliance checking, response generation. AI has moved from a differentiator to a baseline requirement.

The organizations already using AI in their tender management processes are seeing tangible results.

95% reduction in document analysis time. What used to take a team member the better part of a day — reading through a tender package, extracting requirements, mapping lots, identifying eligibility criteria — now happens in minutes. The accuracy is comparable to manual extraction, with the added benefit of consistency and traceability.

10-15 hours saved per tender response. Across the full response cycle — from discovery through submission — AI-assisted workflows are saving teams 10 to 15 hours per tender. For organizations managing dozens or hundreds of active tenders at any given time, the cumulative impact on capacity and throughput is substantial.

These aren't projections. They're measured outcomes from Life Sciences and Healthcare companies using AI-powered tender management tools in production.

AI adoption in tender management isn't uniform across sectors. The data shows clear patterns in who's adopting and where.

Sectors leading adoption. Medical Devices companies account for 62% of requests for AI-powered tender management capabilities, making them the dominant force driving adoption. Pharmaceutical companies follow at 34%, with Diagnostics at 4%. The concentration in Medical Devices makes sense — these organizations typically manage the highest volume of complex, multi-lot tenders with detailed technical specifications, making them natural early adopters of automation.

Most active markets by demand. Europe and MENA are the most active regions for AI-powered tender management. This reflects both the maturity of public procurement frameworks in these regions and the growing complexity of regulatory requirements that make manual processes increasingly untenable.

Early adopters by country. Italy, Spain, and Germany are leading country-level adoption. These are markets where tender volumes are high, documentation requirements are demanding, and organizations have been looking for scalable alternatives to manual processes for years.

Not all AI capabilities are created equal in the eyes of tender management teams. The data reveals clear preferences — and some surprises in growth rates.

The top six AI use cases by demand, ranked by volume:

1. Opportunity detection — the most established use case, with 30% year-over-year growth. Finding relevant tenders across markets and languages remains the foundational need, and organizations are increasingly demanding semantic matching rather than keyword-based alerts.

1. Document analysis — growing at 80% year over year. The ability to automatically extract and structure requirements from complex tender packages is rapidly becoming a must-have, driven by the increasing volume and complexity of tender documentation.

1. Go/No-Go support — the fastest-rising area after response generation, with 120% YoY growth. Organizations want data-driven decision support for bid qualification — helping teams assess which tenders are worth pursuing based on historical win rates, resource requirements, and competitive positioning.

1. Compliance check — 100% YoY growth. Automated verification that a bid meets all tender requirements before submission. Given that compliance failures are one of the most common reasons for bid disqualification, the demand for systematic, automated checking is unsurprising.

1. Response generation — the fastest-growing category at 250% year over year. AI-assisted drafting of tender responses, grounded in historical data and approved content, is seeing explosive demand. This reflects growing confidence in AI's ability to produce contextually appropriate, domain-specific content — not generic text, but structured responses aligned to specific tender requirements.

1. Predictive analytics — 50% YoY growth. Using historical data to predict win probability, optimize pricing, and prioritize resource allocation. Growth is steady but lower than other categories, likely because it requires a mature data foundation that many organizations are still building.

An important signal about where the market stands is the split between organizations adopting AI-powered tender management for the first time and those enhancing existing platforms.

68% are moving from manual or Excel-based processes. The majority of demand is coming from organizations that have been managing tenders with spreadsheets, email, and shared drives — and have reached the limit of what manual processes can handle. For these organizations, the move to an AI-powered platform represents a fundamental shift in how they operate.

32% are enhancing existing platforms with AI. A smaller but significant segment is already using tender management software and now wants to add AI capabilities. These organizations have solved the workflow and centralization problem but are looking for the next level of automation and intelligence.

This split tells us that the market is still in relatively early stages — the majority of potential users haven't yet adopted any purpose-built tender management platform, let alone one powered by AI. The opportunity for further adoption is large.

The data paints a clear picture: AI in tender management has crossed the threshold from emerging technology to operational necessity. The organizations that are adopting it now are seeing real, measurable benefits. The organizations that aren't are falling behind — not in some theoretical future, but today.

For Life Sciences and Healthcare companies specifically, the convergence of increasing tender volumes, growing documentation complexity, and rising quality expectations makes AI adoption not just attractive but essential. The question isn't whether to invest in AI for tender management. It's how quickly you can deploy it — and whether you do so before your competitors do.