How AI is Redefining Value Models for Modern Businesses

A New Approach to AI in Business

Companies often tend to approach artificial intelligence (AI) through individual projects, such as pilots or specific tools, hoping to achieve quick wins. However, this method, while it may yield occasional successes, fails to fundamentally transform the way an organization creates value. This situation is reminiscent of the early days of the Internet, when companies focused on elements like banner ads and automated emails, without grasping the magnitude of the e-commerce revolution.

Organizations that manage to fully leverage AI adopt a broader and more ambitious perspective. They do not see AI as a series of disconnected experiments, but as a coherent set of value models. Each model has its own economic dynamics, timeline for generating value, and governance requirements. This approach not only maximizes the value of each model but also facilitates the integration and expansion of subsequent models.

From Project Management to Portfolio Management

In the current context, five distinct AI value models stand out in the business world. Each of these models creates value in a unique way, with its own economic, temporal, and governance characteristics. Furthermore, each model lays the groundwork for the development of the next.

• Empowering the Workforce: It promotes operational fluidity.
• Fluidity: It makes governance more achievable.
• Governance: It allows for deeper integration of systems.
• Integration: It facilitates the management of dependencies.
• Dependency Management: It ensures the security of agent-driven operations.

Thus, companies can transition from isolated AI successes to broader business transformation. The crucial strategic question is not so much which model to choose, but rather which one to start with, what foundation it establishes, and what opportunities it opens up next.

1. Empowering the Workforce

This model is the quickest to implement. It involves disseminating practical AI capabilities within the workforce, generating short-term productivity gains while preparing the ground for deeper transformation. The real advantage lies not only in accelerating tasks like writing or analysis but in preparing the organization to integrate AI more broadly. Human resources can empower employees, the legal department can ensure governance, finance can allocate necessary resources, and sales teams can collaborate effectively with a shared understanding of AI applications.

Success Indicators:

• Frequency of use by role and skill level
• Reusable prompts and workflows across teams
• Evidence of cross-functional empowerment
• Emergence of new working methods

Common Risks:

A divide within the workforce, where a small group of advanced users progresses while the rest of the organization lags behind.

Leadership Strategy:

Establish a network of champions and initial workflows, such as performance assessment, contract management, and the purchasing process, to make best practices accessible and inspiring.

2. Native AI Distribution

This model is crucial because AI is changing how customers discover, evaluate, and choose products and services, with an unprecedented level of engagement. In AI-native channels, conversion increasingly occurs within a conversation. This shifts the focus from merely increasing reach to building trust and presence at the moment of purchase intent. Companies that succeed will not just be the most visible, but those that can be helpful, credible, and present at the right time.

Success Indicators:

• Qualified intent and number of iterations before user engagement
• Quality of conversion, including retention, upselling, and lifetime value
• Trust signals such as return behavior, repeated engagement, and recommendations
• Activation of data connectors or dedicated applications related to your business

Common Risks:

Treating native AI distribution like an old demand funnel, optimizing for volume at the expense of relevance and lasting trust.

Leadership Strategy:

Choose a surface such as a vertical experience, an integrated application, or a specific advertising objective, and define the quality of conversion before increasing your investment.

3. Expert Capability

This model focuses on integrating specialized AI capabilities into research, creative, and domain-intensive work. In the short term, it helps reduce bottlenecks related to expertise. Over time, it transforms the very nature of expert work, enabling closer collaboration between AI and human professionals.

Success Indicators:

• Reduction in timelines for research and creative processes
• Improvement in the quality and accuracy of results
• Increased capacity to manage complex projects

Common Risks:

Over-reliance on AI without adequate integration of human knowledge, which can lead to errors or biases in results.

Leadership Strategy:

Encourage a collaborative approach between human experts and AI systems, focusing on continuous training and adapting processes to leverage the combined strengths of AI and human expertise.