Interestingly, more than half of those investments have been in European-headquartered businesses—and, despite the volatile macroeconomic climate globally, we see a strong opportunity to fund many more European businesses in the future. 

As Battery looks to increase its investment activity in Europe, and better support its existing portfolio in the region, we are bolstering our on-the-ground, ITLST presence in London. Stefan Momic, one of our vice presidents, recently relocated to London to better support our European activity and is leading recruitment efforts to build out a local investment team in Europe.  

Why Europe? 

In our experience, the region has consistently proven to be a very fertile ground for innovation in industrial tech and life-science tools. Europe has a deep-rooted culture of high-end technology addressing critical trends – from meeting new environmental regulations spurred by climate change and concerns about contaminants like PFAS (“forever chemicals”), to increasing automation to address labor shortages, to contributing to scientific research at the forefront of fighting global diseases.  

European ITLST companies are also, we believe, well-aligned with the value Battery can provide. Most of the businesses we back in countries like Germany, the Netherlands and the U.K., among others, are decades old and most often, family- or management-owned. Often, they’re venerable national champions who realize that to continue growing, they need to start competing outside their home market, which brings a new suite of challenges as they look to flourish in an increasingly globalized world.  

Specifically, these companies may see the need to expand into new geographies or business sectors; up-level their management teams across locations; upgrade technology systems; or increase R&D investment to develop new products and grow organically. Battery offers support in all these areas and offers family- and management-owned businesses a different path for the business and its employees rather than selling out to larger companies. Additionally, Battery helps with the identification, origination and execution of add-on acquisitions that complement and often accelerate organic growth initiatives. 

Many of these companies operate in niche markets, but they frequently make critical, often under-the-radar, technology to enable solutions in important areas like food and water safety, medical innovation and next-generation industrial safety, to name a few. Our most recent European ITLST investments, for example, are steute*, a founder-owned provider of switches and sensors for critical medical and industrial-safety applications, and Skalar Analytical*, a management-owned provider of automated laboratory analyzers for environmental, food & beverage and energy applications.  

Our European footprint 

In Europe to date, our ITLST practice has invested in 10 “platform” companies, and 55 total companies including add-ons since 2003 across 12 different countries, deploying more than EUR 750 million in capital. 

Spotlight: Laboratory tech   

Our investment approach is very thesis-driven and thematic. As an example, the “laboratory tech” space is one sector we’ve focused on, with our flexible mandate allowing us to partner with a wide range of companies serving laboratory customers. Since 2018, we’ve backed European companies across a variety of different business models in this sector, including instrumentation (SPT Labtech* and Skalar*), consumables (AnalytiChem*), services (ifp Labs*) and software (Titian Software*). Skalar*, Bernd Kraft* (starting point for AnalytiChem*), ifp* and Titian* were all founder- or management-owned businesses. 

Our strategy for each of these businesses includes continued investment in organic growth initiatives, plus a healthy mix of add-on acquisitions. At Skalar, for example, we helped the management team identify and execute five complementary founder-owned add-on acquisitions expanding Skalar’s geographic footprint in North America (EST Analytical* and PromoChrom*) and Asia (GERSTEL*’s Japanese and Chinese subsidiaries), while continuing to strengthen Skalar’s European footprint (GERSTEL*, TE Instruments* and LCTech*). On the organic-growth side, having a global sales channel provides accelerated expansion opportunities for all brands. Additionally, we are supporting numerous new product-development efforts at the company, particularly around software and consumables as we look to increase high-quality revenue. We’ve followed a similar global acquisition strategy with Germany-based AnalytiChem, completing seven add-on acquisitions across six countries – Belgium, the Netherlands, the UK, Canada, USA and Australia – in a little over two years.  

In contrast, our strategy for ifp Labs is much more regionalized, focused solely on the pan-European market. To that end, our M&A efforts have been more focused on Europe, with one of our acquisitions expanding ifp’s capabilities in the water testing space, while the other three acquisitions expanded our food quality laboratory network in the UK. All seven add-ons at AnalytiChem and all four add-ons at ifp Labs were family- or founder-owned as well. 

Our strategy and approach is not one-size-fits-all; it is highly tailored to the specific circumstances and desires of a given company’s  management team. We believe our experience with family- and management-owned businesses across a wide range of geographies, business models and end-markets provide a strong foundation for us to support our companies. 

The road ahead 

We continue to be excited about the investment opportunities in Europe across a wide range of industrial tech and life science tools markets. The market overall continues to be highly segmented, with domestic champions on the cusp of turning into global and pan-European leaders through acquisitions and organic growth. We are excited to support these innovative companies through the next phase of their growth journey—please feel free to get in touch if you would like to speak with us. 

The post The Opportunity in Industrial Tech & Life Science Tools in Europe appeared first on Battery Ventures.

The most notable example is Windsurf. The promising AI company was set to be acquired by OpenAI, but then Google swooped in and paid $2.4 billion for a few senior executives and a technology license. But this wasn’t the first instance of this new tech-deal structure. We have now seen it executed at least five other times in the last two years, and the whisper in the industry is that other companies have been approached about similar tie-ups.  

The details of these deal dynamics, and their impact on company employees and investors, have been hotly debated in the press and on social media. But it’s worth exploring why this new wave of AI M&A is happening—and how conditions need to evolve for tech M&A to get back to normal. Put another way: When will big tech acquirers start buying AI companies not just for their teams, but for their durable business moats? 

To be sure, traditional M&A is still happening: Google is in the process of closing its $32 billion acquisition of Wiz, for example. But it’s instructive to compare that deal to the Windsurf transaction. The latter company was a legitimate contender in the AI coding space, a category proving to have one of the strongest signs of product-market fit. The company was growing quickly, hiring aggressively, and had real enterprise customers.  

So why didn’t Google want to acquire the whole thing? In our view, it likely comes down to two factors: 1) The incredibly rapid pace of innovation in AI, which is faster than any previous technology wave we’ve seen, and 2) the scarcity of top-level talent to run cutting-edge AI businesses today. When those dynamics change, and more-mature companies with deeper competitive moats (and more skilled executives) develop, we will likely see a return to pre-AI M&A trends. 

From eyeballs to AI research teams 

Our overall belief is that the current wave of Windsurf-like, talent-license deals are a clear indicator of how early we are in the AI cycle. During the dot-com bubble of the late 1990s and the early mobile era of the 2010s, M&A was often valued based on “eyeballs” and talent that could drive distribution. Today, these deals are valued based largely on the caliber of a company’s elite research team, not enterprise value. The frontier of AI is still being pushed by labs and research teams with immense talent density. Maturation will come from two primary sources: 

• Deeper vertical integration: As startups move from being thin wrappers around foundational models to becoming “fat applications,” they will build more defensible value. This involves owning more of the stack, from data ingestion and processing to model fine-tuning and deployment. For example, companies like Cursor have built their own models for certain features, like Tab, that far exceed the outputs from the AI labs. Meanwhile, Sierra continues to push how much infrastructure it can own by building its own agent-orchestration framework to optimize inference across its system. As companies own more proprietary aspects of their respective stacks, value will accrue to their unique IP. 

• Uncovering the new interaction layer: Many AI-native applications today are built within the UI layers of existing systems and applications. Where people do work, where they orchestrate agents or interact with AI systems will matter in the future, and unlocking this new layer of engagement will matter as well. As we shift from innovating at the infrastructure layer to the application and UI layer, we will see the change in which value accrues among companies as well. 

The coming democratization of AI talent 

The current deal structures are a direct reaction of an incredibly competitive AI labor market for a small pool of elite AI researchers and AI-native builders. These individuals are seen as non-reproducible assets, making their acquisition the primary goal in M&A. When technological breakthroughs depend—as they do in AI today—on novel techniques in data labeling, training, compute optimization, and model architecture, the value in these techniques resides in the talent that can produce these outcomes, not necessarily the outcomes themselves. 

As AI development becomes more democratized through innovation, the open-source ecosystem, and standardized platforms, the acute scarcity of top-tier talent will lessen. When a team is no longer a rare commodity that can only be acquired whole, the rationale for talent-centric deals will fade. Value will shift back to the company as a holistic entity: a collection of talent, technology, and market traction. While the talent game will likely dictate the market for the next 12-24 months, the democratizing force of AI will ensure that speed of progress and slope of learning will always outpace the sheer volume of talent at any single organization. 

Build a moat 

Product moats today are fleeting, as the pace of AI innovation far outpaces any single company’s ability to create lasting defensibility. For strategic M&A to flourish, acquirers need to see durable competitive advantages worth buying. It is not that tech giants are unwilling to make large, strategic acquisitions. In addition to Google’s planned acquisition of Wiz–a prime example of an acquisition made for the traditional reasons of superior tech, talent, and traction—Databricks* has been an active acquirer recently, buying MosaicML for $1.3B, Tabular for $1B, and Neon for $1B. Databricks has integrated these key assets to build a comprehensive data intelligence and AI platform. 

These deals were justified by the targets’ strategic value, not just their talent. We believe AI-company moats will evolve in one or more of the following areas in the coming years, helping these startups become more viable acquisition candidates: 

• Proprietary data: Companies with unique, high-quality data that creates a feedback loop will help improve AI models in a way competitors cannot replicate. 

• Distribution & network effects: A large, engaged user base makes the product more valuable as more people use it. 

• Workflow integration: Deeply embedding an AI tool into a critical business process makes it difficult and costly for customers to switch or meaningfully changing the outcome of a business process. This makes the product “sticky” and boosts revenue. 

The path forward 

The structures influencing the market today will inevitably change. While talent-licensing deals offer speed, they lack certainty for the ecosystem. The rise of private-to-private M&A, led by companies like Stripe (which acquired stablecoin platform Bridge for $1.1B) and Databricks, is a positive signal, indicating a healthy market where growing companies acquire others for strategic capabilities. 

Furthermore, the governance structures between founders and investors will likely evolve, with greater scrutiny placed on key-person clauses and terms that could trigger a premature liquidity event (i.e., does a non-exclusive license trigger protective provisions for investors?). These hybrid structures are inherently flawed, in our view, as they can erode trust. But with the stakes involved, they represent a workaround that will likely make its way into the ecosystem. The real sign of a maturing market will be when acquiring a company is once again about buying a kingdom, and not just hiring its court. 

The post Kingdoms and Courts: The “Windsurf Effect” and How We Get Back to Normal in Tech M&A  appeared first on Battery Ventures.

But as channels have become more saturated, the returns on high-volume, outbound sales have diminished. Conversion rates are dropping, quotas are being missed and sales teams are starting to ask a hard question: Does all this still work?

We don’t think so. And we believe a new GTM platform is emerging to take its place.

From art to science

In the next era of outbound sales, we believe success will come not from sending more messages. Instead, it will come from sending the right message to the right person at the right time, in a system powered by data and intent signals rather than guesswork. What was once a creative art is becoming a data-driven science.

While GTM teams recognize this and want to adapt to this change, unfortunately, the infrastructure to support that transition has not kept up. Over the past decade, companies have invested heavily in siloed prospecting and demand generation tools: data vendors like ZoomInfo, LinkedIn and G2, account-based marketing products like 6Sense* and Demandbase and engagement tools like Salesloft and Outreach. The result is a fragmented stack and scattered signals, forcing GTM teams to spend more time stitching together disparate workflows and systems than actually engaging buyers.  For many reps, the job has shifted from selling to managing tools.  What should be time spent “on the floor” is instead spent collecting data and wrangling software.  One of the most common complaints we hear from reps today is that their job has become learning a sales tech stack, not selling.

Meanwhile, AI-generated messaging has added more noise to the problem, adding more volume and creating a distribution bottleneck as outbound channels have become more saturated with generic outreach that often doesn’t land with customers. The result has been bloated SDR teams with diminishing returns.

What’s changing

There is a shift underway in how go-to-market teams think about outbound. This shift is catalyzing a new class of GTM software companies to emerge. Underpinning this trend are some new realities in sales:

1. The nature of sales work is becoming more technical. GTM teams are shifting away from manual outreach and headcount-driven models towards a more data-first, systems-oriented approach.  The individuals driving outbound today are not just sending more emails, they are building workflows, integrating data and managing automation to scale personalized engagement.  As outbound becomes more intelligent, GTM teams need new tools designed for people who think in systems and data, not for junior reps executing one-off tasks.
2. Pipeline is the bottleneck. Most teams today are struggling to generate enough pipeline, but the problem runs deeper than volume. Much of the pipeline being created is low quality, randomly sourced and lacking in prioritization. Signals are ignored, leads are unqualified, and reps are spending time chasing accounts that were never a fit to begin with. As a result, conversion rates are falling and customer-acquisition cost is climbing. The real challenge is not just getting more pipeline; it’s building the right pipeline that aligns with a company’s ideal customer profile.
3. Timing and context are everything.  In outbound, success hinges not just on who you reach out to, but when you reach out to them and why.  Buyers are overwhelmed with generic outreach, and static account lists don’t cut it.  Modern GTM organizations need to act on real-time signals, like product usage, shifts in hiring, content engagement, relevant news and funding activity, and pair them with contextual, personalized messaging.  Without precise timing and contextual relevance, even well-targeted outreach falls flat.
4. Generative AI is shifting from augmentation to automation. Large language models are now capable of handling tasks that previously required human reasoning and judgment. At the forefront of this shift are AI agents, autonomous systems that can plan, execute, and adapt across complex workflows with minimal human intervention. Processes that once took a full marketing and SDR team to create and complete can now be orchestrated by a single operator leveraging agents embedded in AI-native platforms like Unify. This shift is not just about efficiency. It is fundamentally expanding the opportunity for modern, AI-native GTM platforms by capturing not only software spend but also headcount budgets that the previous generation of GTM tools couldn’t access.
5. Sales teams want consolidation. Point solutions created complexity. GTM teams are now looking for end-to-end platforms that unify the workflow across data, intent, enrichment, engagement and measurement.

Why we invested in Unify*

With all of this in mind, we are excited to announce that Battery led a $40M Series B in Unify*.

Unify is building the end-to-end system of intelligence and action to power warm outbound. The platform brings together AI agents, data, people and actions in one unified workflow, enabling go-to-market teams to identify the right prospects, personalize outreach and execute targeted, high-quality outbound campaigns with minimal human oversight.  Sales reps are then freed to do what they uniquely do best, which is building relationships, not spreadsheets or sending thousands of cold emails.

In under two years, Unify has seen notable growth and is currently supporting high-performing teams at companies like Airwallex, Cursor, Perplexity, Together AI and Flock Safety. The product is designed for today’s innovative GTM operators, specifically those who think in systems and want to scale outbound intelligently, not through brute force.

The co-founders, Austin and Connor, bring a rare combination of product, GTM, and deep technical expertise to this problem. Austin was a leader on the growth product team at Ramp, building systems that scaled personalized outreach through data and signal. Connor, a ML Research Engineer at Scale, built complex AI and data infrastructure. Their experience positions them to rethink outbound for the AI era, not just to automate outbound, but to make growth more predictable, repeatable and creative.

We believe Unify represents the next chapter in GTM software.

*Denotes a Battery portfolio company. For a full list of all Battery investments, please click here.

The post Outbound is Broken — and Why We’re Excited About Unifying the Sales Workflow appeared first on Battery Ventures.

But what does AI-native mean? It’s a term that quickly became ubiquitous, appearing in marketing materials, new launches and pitch decks. Internally, we’ve refined the definition to mean products designed and built with both LLM-powered automation and humans as first-class citizens. In some cases, this could mean a product feature designed with an AI copilot to help drive efficiency in high-value work. In other cases, AI will be used to help automate rote and manual tasks that would traditionally keep someone in the office late.     

Through speaking with hundreds of founders across multiple sectors, procurement stood out to us as a function where these principles can be applied effectively. Having spent more than two decades in the space after investing in SupplierMarket* (acquired by Ariba) during the dot-com boom and then leading Coupa’s* Series B back in 2008, we’ve had a front-row seat to the transformation of procurement from a back-office function to a strategic lever for business performance. The first wave of procurement tools played a pivotal role in bringing visibility and control to spend management across large enterprises. And what has become increasingly clear is that the next wave of innovation in procurement should be AI-native.  

Why? LLMs are effective at parsing and organizing unstructured data, making LLM-powered automation a natural fit for the type of diverse content procurement teams work with every day, including invoices, RFPs, and supplier questionnaires. RFP generation, supplier enrichment, and invoice analysis will all, in our view, become table stakes for procurement teams. But where would a company start in building an AI-native procurement platform? We think spending years growing to appreciate the pain points of procurement teams is, in our view, the perfect prerequisite.  

As we evaluated companies across the market, Alex Yakubovich and Stan Garber at Levelpath* shined in their forward thinking on how to become AI-native and build lasting products for the next decades. Prior to starting Levelpath, Alex and Stan co-founded ScoutRFP, a popular sourcing and supplier platform that sold to Workday in 2019. The experience gave Alex and Stan firsthand experience understanding the pain points of procurement professionals.  

With a big vision, the team came back together in 2022 to build a unified procurement suite that is both mobile- and AI-native, a move intended to disrupt a category of strong legacy products and new startups. A key tenet of this mission is Levelpath’s supplier data model, which delivers a unified user experience across procurement applications and better AI integration. This integration allows for proactive supplier insights, dynamic risk detection and seamless tracking across every phase of the procurement lifecycle.  

Rather than bolting AI on top of legacy architecture, Levelpath embeds AI deeply into both user workflows and backend logic, aiming to automate intake, supplier evaluation, RFP creation and contract management. The team hopes to end the days of poorly integrated, manual solutions in procurement and has been working hard over the last several years to build an extensive platform with early customers.  

We’re excited to announce that we’re leading a $55M+ Series B round and will be working with Stan, Alex, and the entire Levelpath team in their effort to build  a high-performing, AI-native procurement platform. Our conviction stems from the team’s pedigree, early customer wins and what we see as broader market demand for a solution to disrupt the large, underserved procurement ecosystem. As the capabilities of AI continue to grow and evolve, we believe Levelpath will help shape what AI-native means in practice for procurement.   

The post What It Means to Be AI-Native: Our Investment in Levelpath appeared first on Battery Ventures.