AI is rewriting the enterprise software business model

Key Takeaways

• The traditional enterprise software business model is being disrupted by the rise of AI and data-driven decision making
• Companies need to develop a clear AI strategy, assess their organizational readiness, and evaluate their infrastructure to succeed
• The cost of using AI-powered software is no longer predictable and is increasingly based on activity, such as AI inference, API calls, and computational consumption
• Executives must understand the financial consequences of AI adoption and make informed decisions about governance and deployment

Introduction to AI-Driven Enterprise Software

The way companies purchase and use enterprise software is undergoing a significant transformation. For decades, the process was straightforward: executives selected the platform, procurement negotiated the contract, and finance approved the budget. However, with the rise of AI, the value of data is breaking this traditional model. According to China Widener, vice chair for technology, media, and telecommunications for Deloitte's U.S. practice, companies that succeed in the next several years will be those that make better executive decisions about AI governance.

Challenges in AI Adoption

There are three common issues that organizations face when adopting AI:

1. Lack of clear strategy: Up to 80% of companies are in the early stages of thinking about their AI strategy or have not yet developed a clear plan.
2. Organizational readiness: Companies must determine whether they are prepared to deploy AI at scale and whether their employees have the necessary skills.
3. Infrastructure readiness: Organizations must assess whether their infrastructure is ready to support AI adoption, including the ability to handle increased computational consumption and data access.

Comparison of Traditional and AI-Driven Software Costs

Impact of AI on Enterprise Planning

The shift to AI-driven software is changing the economics of enterprise software. Instead of paying primarily for users, organizations are now paying for activity. This means that the cost of using AI-powered software is no longer predictable and can vary significantly based on usage. Executives must understand these changes and make informed decisions about AI governance and deployment to avoid creating unforeseen cost structures.

Bottom Line

The rise of AI is disrupting the traditional enterprise software business model, and companies must adapt to succeed. By developing a clear AI strategy, assessing organizational readiness, and evaluating infrastructure, executives can make informed decisions about AI adoption and deployment. With the shift to activity-based pricing, companies must also understand the financial consequences of AI adoption and plan accordingly to avoid unforeseen costs.