AI presents a wealth of opportunities for businesses, but the way companies leverage it depends on their strategic intent—whether they seek to enhance efficiency or drive new growth and whether they execute within the corporate structure or through a new venture. This results in four distinct pathways, which are detailed in this article. We also highlight why we think corporates are currently missing out on a big opportunity. Read on to learn more about AI ventures and how to pursue them.

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1. Efficiency projects – Optimizing the Core

Ambition: Incremental improvement

Intention: Enhance existing processes and cost structures

Corporates leveraging AI for efficiency focus on automation, process optimization, and operational excellence. This approach is about doing things better—reducing costs, improving speed, and increasing reliability within existing business functions. AI applications in this category range from predictive maintenance in manufacturing to AI-driven customer service chatbots. The primary driver here is cost efficiency and performance gains (bottom-line focus), without fundamentally changing the business model.

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2. Upgraded or new Products – AI-Enabled Growth

Ambition: Business Expansion

Intention: Strengthen market position, reach new customer segments in existing markets

In this approach, corporates move beyond efficiency and embed AI into their core offerings to unlock additional growth. This could mean developing AI-powered new products or integrating AI-based capabilities into existing ones. For instance, a retail company integrating AI-powered personalization into its e-commerce platform or a bank deploying AI-driven financial advisory tools. The focus here is leveraging existing assets and customer bases to scale AI-driven innovations within the corporate framework.

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3. New Value Proposition – AI-Powered Disruption

Ambition: Market and industry disruption

Intention: Build an AI-first business that challenges the status quo

New ventures that leverage AI for industry disruption aim to change business operations fundamentally. These ventures start with AI at their core, developing novel business models that wouldn't be possible without AI. Examples include autonomous logistics companies, AI-driven drug discovery startups, and algorithmic trading platforms. This pathway is high-risk but offers high-reward potential by reshaping industries, creating entirely new markets, and bypassing traditional limitations.

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4. Efficiency-driven Solutions – Expanding Beyond the Core

Ambition: Diversification

Intention: Generate revenues from internal efficiency projects

This approach involves AI-powered projects that started internally with an efficiency focus (see no. 1 above), but these projects have venture potential. Companies can commercialize the results of an internal efficiency effort to peers and others in their industry and beyond. For instance, a legal tool developed internally to help lawyers find relevant law articles faster can be sold to other branches of law or international markets while relying on the same technology and idea.  

Below, you can find more examples of AI projects in each category to help you better understand the different types and variants.  

Are corporates are missing out?

Looking at the illustration with examples, we see that many venture cases started out as independent startups – not corporate ventures. While it is normal for corporations to adopt new technologies first in known territories, we still think corporations are at risk of missing out on big opportunities. All this, while we see AI as an ideal case and technology where corporate ventures might be at an unfair advantage vs. independent startups. As is the case with all corporate ventures, they only make sense if a strategic asset of the corporate can be leveraged (brand, customer access, technology, etc.). In the AI case, the strategic asset is data, as data is central to all AI and ML efforts. And while independent startups start on field 0, corporates sit on troves of data – albeit of varying types, quality, and usefulness. Nonetheless, data from products in the field, customer insights, or other sources can yield a critical advantage with AI-based ventures.  

Please read this article to learn more about our proven approach for corporates and how to systematically explore AI venture opportunities.

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