AI’s “impact on traditional industries can be immense,” says Q Fund

“In industries that depend heavily on 'tribal knowledge' or subject matter experts, proprietary data often represents the strongest moat we see today,” explained Liav Ben Rubi and Dana Taigman Koren, Managing Partners at Q Fund. “Teams savvy enough to partner with traditional players who can provide exclusive access to this data have a unique opportunity to develop groundbreaking products that can fundamentally redefine how those industries operate.”

Ben Rubi and Taigman Koren joined CTech for its VC AI Survey to share insights on the state of the industry. The fund, which invests at Seed and Series A rounds, has its eyes on the AI space.

Liav Ben Rubi and Dana Taigman Koren

“We believe the impact on traditional industries can be immense. These sectors often generate billions in revenue but operate with very thin margins, so even a small improvement in cost efficiency or revenue generation can have an outsized effect on profitability,” they added. “AI has the potential to unlock those gains, whether through predictive maintenance, process automation, or intelligent decision support. When combined with broader macro trends—like the reshoring of manufacturing, aging workforces, and a shortage of skilled labor—we could see sectors that have remained largely unchanged for decades, if not centuries, undergo fundamental transformation.”

You can read more below:

Fund ID
Name and Title: Liav Ben Rubi and Dana Taigman Koren - Managing Partners
Fund Name: Q Fund
Founding Team: Liav Ben Rubi and Dana Taigman Koren
Founding Year: 2022
Investment Stage: Seed to Series A
Investment Sectors: AI, Cyber, Mobility, Investing in Deep Tech for Automotive, Defence, Cyber, Logistics, Energy and Industrial Transformation

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?

6 - We are currently using various tools and exploring new research applications, including internally testing and building a few tailored solutions for our needs

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

No.

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

Yes, especially as DeepTech investors that are primarily focused on hardware. Over the last two years, we've extensively explored the industrial sector to identify areas where AI integration and new software approaches can create significant gains. We've developed a thesis around how AI adoption can disrupt and transform traditional industrial practices.

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?

We prioritize the rate of product usage, looking beyond initial purchases to understand the percentage of active users. While ARR per employee is popular, it's less relevant for our early-stage investments. We are keen to see gross margin data, ideally at a granular customer level, to assess pricing and cost effectiveness.

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

As DeepTech investors, we are accustomed to Seed/Series A rounds that occur before significant or any sales, primarily based on technological potential and the team's assessed ability to execute.

While traditional financial metrics may be limited at this stage, we look at early signals of product-market fit, such as pilot deployments, design partnerships with strategic customers, or strong inbound interest from industry players. These indicators help us gauge whether the company is solving a real and urgent problem.

Valuation, in this context, becomes a function of potential impact, market timing, and uniqueness. It’s less about current revenue and more about the credibility of the long-term vision—and the company’s ability to lead in its chosen category. We apply the same approach when we are considering an investment in an AI-native company.

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

A high churn rate is a concern, as many users and companies tend to try tools for limited periods

Do you focus on particular subdomains within AI?

No, we are more interested in the specific sector or vertical solution targets and the potential for significant impact on customers using the product.

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

We believe the impact on traditional industries can be immense. These sectors often generate billions in revenue but operate with very thin margins, so even a small improvement in cost efficiency or revenue generation can have an outsized effect on profitability.

AI has the potential to unlock those gains, whether through predictive maintenance, process automation, or intelligent decision support. When combined with broader macro trends—like the reshoring of manufacturing, aging workforces, and a shortage of skilled labor—we could see sectors that have remained largely unchanged for decades, if not centuries, undergo fundamental transformation.

While building enterprise-grade solutions for traditional industries is challenging, we are confident that companies poised to revolutionize quality assurance, safety, procurement, shipping, logistics optimization, compliance, and defense, among others, have already been established or will emerge soon. Israeli startups can excel in any vertical or solution type, but founders and their team must have deep industry knowledge, understand how to build a product, and be able to assure enterprise buyers that they comprehend their unique needs.

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?

We've observed numerous compute exits in the Israeli tech scene, indicating a wealth of talent with fundamental knowledge of both hardware and software computing. I anticipate an acceleration in this trend, leading to more exits in the broader computing sector in the coming years. Another niche that could significantly strengthen is micro-applications for B2C/B2SMB, which might see double-digit exits with minimal funding and shorter development cycles (e.g., Base44).

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?

We are seeing a growing number of startups focused on traditional/industrial segments, though they still represent a small percentage of all new "AI" startups. While customer demand is high, these segments present unique challenges, including long sales cycles, on-premise deployment requirements, and different work cultures.