AI Venture Program: how corporates structure their AI investment strategy

In short: an AI Venture Program is far more than an AI-oriented R&D budget. It is a structured investment architecture that combines capital, commercial partnerships and governance in order to capture the best AI startups ahead of the competition. This guide sets out how to structure it, steer it and measure it.

Introduction: why corporates can no longer ignore AI startups

Artificial intelligence is no longer a topic for the future — it is a competitive reality of the present. In 2026, companies that have not yet structured an approach to investing in or partnering with the most promising AI startups are already carrying a measurable strategic lag.

But investing in AI startups cannot be improvised. The most frequent mistakes Mandalore Partners observes in corporate AI programmes include: scattered investments with no coherent thesis, pilots that never scale for lack of governance, and startups selected on technology criteria without any validation of product-market fit.

This guide sets out how the most advanced corporates structure their AI Venture Program in 2026 — from defining the thesis to measuring KPIs, by way of partnership models and investment structures.

1. What is an AI Venture Program?

An AI Venture Program is a structured initiative through which a corporate organisation invests in AI startups aligned with its strategic objectives, or builds deep commercial partnerships with them, with the aim of transforming its operations, creating new products or accelerating its sector competitiveness.

An effective AI Venture Program rests on three complementary pillars:

2. Why 2026 is the moment to act

Consolidation of the AI market is creating entry windows

After the euphoria of 2021-2022 and the correction of 2023, the AI startup market has gone through a natural selection. The players still standing in 2026 show robust growth metrics, paying customers and a path to profitability. Valuations, although recovering, remain more reasonable than in 2021 — creating attractive entry points for strategic investors.

Generative AI is verticalising sector by sector

In 2026, generative AI is no longer a horizontal phenomenon: it is verticalising. Startups are developing LLMs and AI agents specialised for insurance (claims automation), finance (risk modelling, fraud detection), industry (predictive maintenance, quality control) and healthcare (diagnostic support, molecule discovery). These verticals correspond precisely to the sectors of Mandalore Partners' 4i strategy.

Competition for the best AI startups is intensifying

Tier-1 European VC funds (Balderton, Northzone, Speed Invest, Partech) and American ones are already positioned on the best AI opportunities. Corporates that have not yet structured an investment programme risk being shut out of the most attractive rounds — or gaining access only on less favourable terms.

Key figures, AI Venture Europe 2026

AI VC investment in Europe: +42% in 2025 vs 2024 (Source: Dealroom)

Number of active European AI startups: more than 4,200

Top 3 most funded AI sectors: HealthTech, FinTech, Industry Tech

Median Series A valuation, AI Europe: €28 million (vs €18 million in 2023)

3. The 6 steps to structuring your AI Venture Program

Step 1 — Define your AI thesis

Before investing a single euro, your organisation has to answer three fundamental questions:

• What are your most urgent operational challenges that AI can solve?
• Where can AI create a durable competitive advantage in your sector?
• What kinds of startups are you looking for: commercial partners, technology suppliers or pure investments?

The thesis has to be precise enough to guide selection (e.g. "B2B AI startups improving underwriting or claims in P&C insurance in Europe") without being so restrictive that it excludes the most promising adjacent opportunities.

Step 2 — Choose the right engagement model

There are four possible engagement models with AI startups, in increasing order of intensity:

Step 3 — Define the budget and the structure

The budgets of AI Venture Programs vary considerably depending on the ambition and the size of the organisation. Here are the ranges Mandalore Partners observes across its European mandates:

• Exploration programme (less than €2 million a year): 3 to 5 commercial partnerships or pilots, with no equity investment. Ideal for testing the approach.
• Target investment programme (€2 million to €10 million a year): 2 to 5 direct investments through an SPV or a dedicated fund. Recommended for corporates with a clear thesis.
• Institutional programme (€10 million to €50 million a year): a dedicated fund run under VCaaS or as a semi-internal CVC, with a portfolio of 10 to 20 startups over a 7-year horizon.

Step 4 — Source the best AI startups

Deal flow is the central challenge of any startup investment programme. The most effective sources for AI Venture Programs in 2026:

• Specialised VC networks: the funds already investing in your vertical are the best referrers.
• Sector accelerators: Station F, Entrepreneur First, The Family, or corporate accelerators (AXA Next, BNP Paribas Plug and Play).
• AI events: VivaTech, AI Summit London, Web Summit, NeurIPS.
• A VCaaS partner: the most effective route to immediate proprietary dealflow, pre-screened and aligned with your thesis — that is the core value proposition of Mandalore Partners.

Step 5 — Structure AI due diligence

Due diligence on an AI startup requires an adapted assessment grid, on top of the classic VC criteria:

• Data quality: an AI is only as good as its data. Assess the ownership, the quality and the exclusivity of the datasets used.
• Technological defensibility: is this a GPT-4 wrapper or a proprietary architecture? What is the real barrier to entry?
• AI Act regulation: compliance with the European AI regulation, in force since 2024. Particularly critical for applications in healthcare, insurance and finance.
• Scalability of the business model: are inference costs (GPU, cloud) absorbable at scale? What is the path to profitability?
• Team: expertise in ML/ML Ops, and the ability to hire AI engineers in a highly competitive market.

Step 6 — Define KPIs and portfolio monitoring

An AI Venture Program without measurable KPIs is bound to be abandoned at the first change of leadership. The metrics recommended by Mandalore Partners:

4. The 5 most frequent mistakes in AI Venture Programs

Mistake 1 — Investing without a defined thesis

Many corporates start by investing in "interesting" AI startups without a strategic framework. The result: an incoherent portfolio with no synergies, and internal LPs questioning the value the programme adds.

Mistake 2 — Confusing a POC with a strategic partnership

Multiplying POCs without a process for moving to scale is a classic mistake. Every POC has to be designed with predefined success criteria and a clear path to either integration or abandonment.

Mistake 3 — Underestimating the time AI due diligence takes

Assessing an AI startup takes 2 to 3 times longer than a conventional startup, because of the technical and regulatory complexity. Failing to plan adequate resources leads to rushed decisions.

Mistake 4 — Neglecting SFDR/CSRD reporting

Institutional investors and finance departments increasingly demand ESG reporting on venture allocations. A programme without an impact measurement framework creates friction with internal governance.

Mistake 5 — Wanting to bring everything in-house too fast

The temptation to "set up a CVC internally" after a few successful investments is strong, but premature. Building a credible VC team takes 3 to 5 years at a minimum. VCaaS makes it possible to capitalise on existing expertise while keeping strategic control.

5. Mandalore Partners' AI Venture programme

Mandalore Partners offers a VCaaS mandate dedicated to AI investment programmes, covering the full cycle:

• Definition of the AI thesis, co-built with your team (1 to 2 framing workshops)
• Proprietary sourcing through our European and international network of more than 2,000 active startups
• Enhanced AI due diligence: technical, regulatory (AI Act), financial and ESG
• Structuring adapted to the mandate: dedicated SPV, co-investment, thematic fund
• Active portfolio monitoring: governance, commercial support, opening up our network
• Impact reporting: aligned with CSRD and SFDR Article 8, compatible with the requirements of your investment committee

Our 4i sector focus (InsurTech, Invest Tech, Impact Tech, Industry Tech) positions us on the AI startups most relevant to corporates in the financial and industrial sectors — with proprietary deal flow that cannot be replicated without years of presence in the ecosystem.

Conclusion

Structuring an AI Venture Program is no longer a question of "whether" but of "how" and "at what pace". Organisations that act in 2026 will benefit from a first-mover advantage on the best AI startups in their sector, from valuations that are still reasonable compared with previous cycles, and from optimal alignment with the operational transformations already under way.

Mandalore Partners' VCaaS model is designed precisely to allow ambitious organisations to start within a few weeks, with institutional discipline and reporting aligned with the requirements of their governance.

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