🤖 A New Era in AI Pricing: Results-Oriented Economy! 🚀

[Erkan Teskancan]

🤔 Why Are Manufacturers Making Missteps in AI Pricing?​

Manufacturers adopting artificial intelligence (AI) technologies are at risk of losing money due to incorrect pricing strategies, rather than achieving the expected return on their investments. Experts state that industry leaders are creating ineffective cost structures for AI agents, which leads to revenue loss.

💸 Incorrect Strategies Leading to Revenue Loss​

Approximately five years ago, industrial companies embarked on the path of becoming software companies by adopting technologies such as sensors, data lakes, and digital twins. However, customers' reluctance to embrace subscriptions led to a decline in revenue from data monetization and a reduction in software margins. Pricing and value expert Stephan Liozu emphasizes that with the advent of AI, these problems have worsened, and executives are about to fall into the same traps.

📈 The Dangers of AI Cost Structures​

According to Liozu, what makes AI different and more dangerous is its cost structure. The cost of an AI feature is not fixed, does not decrease along a regular curve, and is not entirely under your control. Factors such as model selection, token length, context windows, GPU availability, and how frequently the customer's autonomous agent queries the system can cause significant fluctuations in inference costs. In fact, a single enterprise buyer's AI agent could wipe out a quarter's gross profit in a single weekend.

The industry is responding to this situation with tactics such as passing consumption pricing on to the customer, metering tokens, and charging per API call. However, Liozu argues that these techniques are ineffective and that consumption-based pricing for industrial AI is not a business model; rather, it is a sign that a company does not know the value of its product.

🎯 Solution: "Outcome-Based Economy"​

Liozu states that the only defensible path is an "outcome-based economy." In this model, pricing is tied to the measurable results your AI produces in the customer's operations. In industrial markets, outcomes are quite tangible, and this is the biggest structural advantage industrial companies have over their pure software competitors.

These outcomes reside in the customer's ERP and MES systems. When you price according to these, three things change simultaneously:

• The sales conversation shifts from being about software to being about profit and loss.
  
• The risk of AI underperformance reverts to the party best equipped to manage it: you.
  
• The computational cost volatility that scares your CFO ceases to be a customer-facing line item and becomes an internal engineering problem.
  

🔮 Future of AI Pricing: Performance Contracts​

Liozu suggests that this approach will have three significant consequences:

1. Most AI features developed today will not stand up to outcome-based scrutiny. If a company cannot demonstrate the specific dollars its feature puts into the customer's pocket, it doesn't actually have a product.
   
2. The people who should be setting AI prices are not in the product organization but in the operations, reliability, and service teams who spend time at customer sites. They know what an hour of unplanned downtime actually costs.
   
3. The winners of the outcome-based economy will resemble performance contractors more than software vendors.
   

Liozu discusses aspects such as "gain-sharing" and contractual clauses where the supplier earns nothing when customer key performance indicators do not move, but earns a premium when they do. He claims that sophisticated industrial buyers are quietly starting to ask for these in their requests for proposals and will explicitly demand them within 18 months.