No longer confined to research labs or only speculative hype, AI is reshaping industries and unlocking new opportunities for growth, efficiency, and innovation. Yet, the question remains: How can corporations translate this potential into actionable ventures? What is necessary to navigate the complexities of AI-driven opportunities in corporate venturing?

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We developed a four-step approach tailored for corporates seeking to harness AI's power to create impactful and scalable ventures. This approach bridges strategic foresight, industry insight, and disciplined execution to maximize value creation.

Our 4-Step Approach for AI-Based Ventures

Step 1: Deepen Understanding of AI as a Megatrend

Step 2: Analyze Industry Processes and Automation Potential

Step 3: Develop Scenarios and Prioritize Opportunities

Step 4: Implement and Validate Through Corporate Venturing

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Step 1: Deepen Understanding of AI as a Megatrend

AI is not merely a technological advancement but a foundational shift that demands strategic attention. Corporations must view AI as both an opportunity and a risk. While it offers avenues for operational optimization and entirely new business models, ignoring it may lead to erosion of market relevance, increased vulnerability to disruptors, and the obsolescence of traditional products and services.

Dissecting the AI Megatrend

Only when understanding the different elements that make up the AI megatrend will corporations and innovators be able to discover all the hidden potential. We have highlighted the details of the AI Megatrend in this article.

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Step 2: Analyze Industry Processes and Automation Potential

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AI-driven opportunities often lie at the intersection of industry-specific knowledge and technology application. A detailed analysis of value chains within and beyond a corporation’s current vertical integration is critical to identifying meaningful use cases. It is also these cases of corporate ventures that, in many instances, prove to be the most valuable, as they can best leverage corporate assets, providing a critical unfair advantage that corporate ventures can have versus independent startups.  

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Key considerations when looking at an industry value chain:

Automation Feasibility:

1. How frequent and repetitive is the process?

2. What level of precision is required for automation to be effective?

Venture Viability:

1. Performance Impact: How significant is the potential improvement for the business or industry?

2. Value Chain Scope: Which processes or segments are affected? Are we chaining one step or an entire process chain?

3. Market Opportunity: Is there external demand beyond internal needs? Can the performance improvement be commercialized?

This value chain analysis is ideally conducted at multiple levels of abstraction. While low-level analysis will lead to punctual process automation and align with operational efficiency, higher-level abstraction will reveal opportunities of greater significance and highlight opportunities for new value creation, potentially unlocking ventures that transcend the traditional boundaries of a given value chain.

For example, let's look at the ride-hailing value chain at two different levels of abstraction. A closer, less abstract view might optimize the process step of driver qualification & onboarding – optimizing the efficiency within the given value chain. The more abstract, somewhat distant view of service delivery will reveal opportunities with potentially more significant impacts, such as autonomous driving. This could lead to a different offering and business model altogether.  

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Step 3: Develop Scenarios and Prioritize Opportunities   

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Strategic foresight is essential for navigating the uncertainties inherent in AI-driven innovation. AI does not exist in isolation; it interacts with broader industry trends, external market dynamics, and other megatrends, creating a complex landscape of potential futures.

Scenario Planning for AI Ventures

• Construct Futures: Combine insights from AI developments, industry-specific trends, and external drivers to construct plausible hypotheses and future scenarios.

• Identify Opportunities: Map out venture opportunities within each future scenario; what future will give rise to what kind of venture?

• Prioritize: In a two-step process, rank scenarios based on plausibility and venture opportunities based on their feasibility and potential.

This process culminates in a prioritized list of high-impact ventures. A "future-back" approach—starting with long-term visions of ventures and working backward to current actionable opportunities—ensures alignment between visionary goals and practical execution. This will show you what validation step you have to do next and what you can build today to make the future vision a reality later on.

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Step 4: Implement and Validate Through Corporate Venturing

The transition from concept to scalable venture requires a disciplined approach to execution. The Business Idea Journey —a structured methodology for ideation, prototyping, and scaling—provides a framework for navigating the complexities of AI ventures. Even though this is a structured approach that is venture agnostic, AI-based ventures tend to have some specificity due to their technical and data-heavy nature:

• Technical Complexity: Misjudging the capabilities or limitations of AI can derail projects.

• Data Dependency: Ensuring data quality, availability, and relevance is critical for success.

• Cost and Timeline Misjudgments: Underestimating resource requirements can jeopardize long-term viability.

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Mitigating Risks in the Early Stages

The early stages of validation are crucial to reducing uncertainty:

• Define the Task: Clearly articulate the problem the AI solution aims to address.

• Validate Feasibility: Assess whether AI can deliver the required performance and efficiency.

• Project Costs: Account for development, deployment, and ongoing operational costs.

By rigorously testing assumptions and refining concepts, organizations can focus resources on ventures with the highest potential for long-term success.

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Conclusion: From Potential to Realized Impact

AI represents a paradigm shift with far-reaching implications for corporate venturing. However, unlocking its potential requires more than technical expertise—it demands a strategic approach that integrates industry insight, disciplined execution, and an unwavering focus on value creation.

Through the above-presented four-step approach, corporations can not only navigate the complexities of AI but also emerge as leaders in leveraging it to shape the future. The question is not whether to act but how quickly and effectively action can be taken. The time to seize the AI opportunity is now.

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