Viola Ventures on AI’s ‘iPhone Moment’ for traditional industries

“AI is the ‘iPhone moment’ for traditional industries. In sectors like banking and healthcare, we're seeing startups move beyond simply adding AI features to existing software,” said Alex Shmulovich, Principal at Viola Ventures. “Instead, they are automating entire parts of a value chain to deliver a specific, tangible outcome, sometimes even competing against human-heavy incumbents with AI-as-a-service value propositions.”

Alex Shmulovich.

Shmulovich joined CTech for its VC AI Survey, where he discussed the impact of artificial intelligence. “The AI isn't just assisting a human; it's performing a complex function that was previously done manually, such as risk underwriting or clinical trial analysis. This approach is highly transformative because it completely redefines how a company operates and what it can achieve.”

You can learn more in the interview below.

Fund ID
Name and Title: Alex Shmulovich, Principal
Fund Name: Viola Ventures
Investment Team: Zvika Orron, Omry Ben David, Shlomo Dovrat, Avi Zeevi
Founding Year: 2000
Investment Stage: Seed and Series A
Investment Sectors: FinTech, Vertical AI, Cyber, Infrastructure

On a scale of 1 to 10, how has AI impacted your fund’s operations over the past year - specifically in terms of the day-to-day work of the fund's partners and team members?

10 - As a fund, we've gone beyond using simple tools like ChatGPT for basic tasks. We're now building a collaborative AI architecture on top of our data stack, creating "one-click" workflows. This means more efficient deal sourcing by identifying promising founders from online activity and generating high-level research before meetings to make them more substantive. This increased speed, depth, and access are fundamentally changing the 50-year-old VC model. While we're excited by the potential for automation, we're careful to ensure it augments, not replaces, the human touch. The core of venture capital remains a relationship-driven art, and our goal is to use AI to deepen those relationships rather than automate them away.

Have you already had any significant exits from AI companies? If so, what were the key characteristics of those companies?

A great example of an AI playbook is our investment in PhaseV. Their success is rooted in strong founder-market fit, leveraging world-class data and pharma expertise to solve a critical industry pain point: the high cost of failed clinical trials. They built a real data moat from unique clinical trial data and sell the product as a service that delivers a specific outcome, not just a software tool. This vertical AI approach, which automates a complex, regulated workflow and integrates seamlessly into the existing tech stack, allowed them to reach multi-million dollar revenue in their first year. We believe this playbook can be replicated across other traditional industries like banking, education, and defense, in all of which we made early bets in the past year.

Is identifying promising AI startups different from evaluating companies in your more traditional investment domains? If so, how does that difference manifest?

AI startups are dramatically different from their traditional enterprise software counterparts in early traction, flashy products and scalability. But it's not just about wow products and simple vibe coding. It's about fitting AI into existing tech stacks so that legacy companies can adopt easily. We look for AI startups that understand how to blend the new with the old.

Looking at AI infrastructure, we looked at a company that allows data-residency, drastically optimized inferencing on more available hardware, simple model alteration, and streamlines use-case deployment. This task requires a deep kernel-level understanding of operating systems. Israel has a unique talent pool with relevant experience coming from world-class weakness research that translates into building an AI deployment platform that drives traditional industry adoption of AI.

What specific financial performance indicators (KPIs) do you examine when assessing a potential AI company? Are there any AI-specific metrics you consider particularly important?

While high-level KPIs remain similar, expectations have shifted. Early-stage AI startups now need to demonstrate a clear path to high-velocity growth from the outset. This means I closely track KPIs related to the sales cycle, such as deployment and integration speed, top-of-funnel lead generation, and time-to-value for customers. Beyond logo acquisition metrics, pricing is also critical. I advise founders on creating simple, yet effective pricing models that align with the value the AI product delivers. This ensures they can capture value and build a sustainable business from the start, a necessity for meeting the higher revenue run-rate expectations we now have for Series A-stage companies.

How do you approach the valuation of early-stage AI startups, which often lack significant revenues but possess strong technological potential?

We're seeing AI startups continue to command premium valuations, similar to the cybersecurity boom in Israel.

In recent years, Israel has become a cyber-nation. Still, we also have a few AI Infra wins as well as Vertical AI category leaders - Pinecone, RunAI, Deci, and recently the upcoming star Decart and a few other promising candidates that are still in stealth-mode; Via, Tomorrow.io, Immunai, Aidoc, Oddity, to name a few.

Application Companies

These startups either leverage existing models to build new, innovative products (Harvey.ai) or build proprietary models (Lightricks) and drive technology integration with the application layer to drive distribution with the end customer. This category is a mixed bag. Consumer AI applications, driven by viral, product-led growth, can achieve hyperscalability but face a brutally competitive landscape with low customer loyalty. On the other hand, Vertical AI companies, which use AI to disrupt specific industries like healthcare or finance, are gaining traction. Success is granted by building deep industry expertise, creating products that solve specific pain points and deliver tangible value, which allows them to create defensible moats and justify high valuations.

Infrastructure Companies

These are the "picks and shovels" of the AI revolution, creating the infrastructure that allows LLM models to run, such as vector databases, observability, and GPU optimization software. These companies require deep technical expertise, and their valuations are often based on the defensibility of their technology and the ability to secure multi-year enterprise contracts. Their moats are typically strong, making them attractive long-term investments.

Breakout stories can drive triple-digit valuation growth, as with Decart, Duetti, and PhaseV to some extent, justifying a high valuation at the Seed.

What financial risks do you associate with investing in AI companies, beyond the usual technological risks?

Beyond technical challenges, the most considerable financial risk is the difficulty of creating a sustainable competitive moat. In many AI application domains, the commoditization of software development means products can be easily replicated. This leads to minimal customer loyalty and switching costs, making revenue streams volatile and long-term market leadership uncertain.

A key exception is when AI is applied to complex, regulated industries like finance or healthcare. In these sectors, the need for robust integrations and compliance creates high barriers to entry. Companies like Lama.ai, which is building an AI-powered loan risk engine for regional banks, can leverage this friction to build durable moats and secure long-term, predictable revenue streams, mitigating the risks of commoditization.

Do you focus on particular subdomains within AI?

In the following months, I'm increasing my attention on physical AI.

In physical AI, we're bullish on the convergence of sensors, edge processors, and advanced models to build products that interact with the real world. Israel has a strong ecosystem with players like Mobileye and NVIDIA, as well as the defensetech ecosystem, and I'm actively looking to back companies in robotics and manufacturing that leverage this expertise.

How do you view AI’s impact on traditional industries? Are there specific AI technologies you believe will be especially transformative in certain sectors?

AI is the ‘iPhone moment’ for traditional industries. In sectors like banking and healthcare, we're seeing startups move beyond simply adding AI features to existing software. Instead, they are automating entire parts of a value chain to deliver a specific, tangible outcome, sometimes even competing against human-heavy incumbents with AI-as-a-service value propositions. The AI isn't just assisting a human; it's performing a complex function that was previously done manually, such as risk underwriting or clinical trial analysis. This approach is highly transformative because it completely redefines how a company operates and what it can achieve.

What specific AI trends in Israel do you see as having strong exit potential in the next five years? Are there niches where you believe Israeli startups particularly excel?

In the next five years, we anticipate strong exit potential in three key areas where Israeli startups particularly excel. First, the next generation of AI infrastructure companies focused on solving the critical challenges of performance, scalability, and the scarcity of computing resources. The deep tech expertise in Israel is uniquely suited for this. Second, we see great potential in Vertical AI companies that are disrupting traditional, high-value industries like healthcare, banking, and insurance. They build defensible moats by combining deep industry knowledge with powerful AI. Lastly, physical AI companies in defense, logistics, and manufacturing are poised for significant growth by leveraging Israel's world-class talent in both hardware and software to build products that bridge the digital and physical worlds.

Are there gaps or missing segments in the Israeli AI landscape that you’ve identified? What types of AI founders are you especially looking to back right now in Israel?

While Israel is strong in infrastructure and vertical applications, there's a gap in companies that effectively balance deep technical expertise with commercial viability. We're actively looking to back founders with deep industry expertise, either through their own experience or through a robust network of advisors. We also seek out founders who possess unique technical capabilities and a profound understanding of AI models and systems. Finally, we want to back lean, AI-native companies that build scalable products and can effectively leverage our network of Fortune 500 executives as design partners. These are the founders who can not only build incredible technology but also successfully integrate it into a real-world business environment.