AI-Driven Pricing vs. AI-Augmented B2B Pricing — Why Human Expertise Still Matters

Artificial intelligence is pushing boundaries in the software industry, pricing included, but not necessarily in the way you might predict from the genAI boom. Not all AI-powered pricing approaches are created equal. Understanding the difference between AI-driven pricing and AI-augmented pricing is crucial for businesses aiming to optimize revenue without losing strategic control.

AI-Driven Pricing: Fully Automated Decisions

AI-driven pricing models rely predominantly on algorithms making pricing decisions with minimal human intervention. These systems:

• Process large volumes of data to set prices dynamically
• Automatically adjust prices based on market signals and demand patterns
• Minimize human input, aiming for speed and scale

Pros:

• Extremely fast and scalable
• Can capture complex patterns and react to market changes instantly
• Primarily used in B2C environments where demand changes rapidly

Cons:

• Limited contextual awareness—may miss strategic considerations or unique nuances
• Risk of automated decisions that conflict with company policies or client relationships
• Lower transparency, making it harder to explain pricing decisions to stakeholders
  

B2C Examples of AI-Driven Pricing

Online Travel Agencies (OTA) and Airlines

These companies use Ai-Driven pricing in a number of scenarios based on demand or lack thereof.  One OTA will constantly scan for lower prices on flights and hotels and automatically rebook the customer without intervention and give them the difference in future trips credits.  Airlines will dynamically update prices to reflect demand and availability.

Ride-Sharing Surge Pricing
A ride-hailing app uses AI-driven surge pricing algorithms that instantly increase fares during high demand periods. This fully automated system reacts in real time to supply and demand, optimizing platform-wide revenue efficiently.

Real-Time Energy Market Pricing
An energy provider employs AI-driven pricing models to set wholesale electricity prices minute-by-minute, responding to grid supply, weather conditions, and demand forecasts with minimal human input.

In contrast, some B2B domains have already shifted to fully autonomous pricing—most notably, in cloud computing and usage-based platforms.

B2B Example of AI-Driven Pricing

AWS EC2 Spot Instances – Amazon EC2 Spot Pricing
AWS automatically adjusts EC2 Spot pricing based on supply and demand—no human interaction required. Prices fluctuate in real-time, optimizing infrastructure use.

AI-Augmented Pricing: The Best of Both Worlds

AI-augmented pricing combines AI’s computational power with human expertise. The AI suggests optimal pricing and discounting strategies, but final decisions involve human-in-the-loop review and input.

Key Features:

• AI processes data, detects trends, and makes recommendations
• Pricing teams simulate, validate, adjust, or override AI suggestions based on context
• Collaborative workflows support rapid iteration, transparency and trust
  

Benefits:

• Balances speed and scalability with strategic insight
• Ensures pricing decisions align with broader business goals and client relationships
• Builds confidence among sales, finance, and executive teams through transparency

AI-augmented pricing enhances human decision-making by surfacing high-quality recommendations based on massive simulations and data, but not replacing expert judgment.

Real-World Use Cases of AI-Augmented Pricing

Enterprise SaaS Pricing & Negotiations – SPP Levelsetter
LevelSetter models multitudes of pricing model options and makes recommendations that are optimized to best suit the desired business outcomes. Levelsetter flags risky discounting patterns and suggests optimized pricing guardrails. Pricing teams can adjust recommendations based on strategic value, relationship context, or upsell potential.

While AI can process vast amounts of data and detect patterns, humans must still apply strategic, ethical, and relational context across inputs that cannot yet be quantified in data, regular patterns or simulations.

Let’s not forget Generative AI

There is a lot happening with Generative AI right now and the race is on to see who will be the victor in next generation search.  But the reality is that while this technology is providing significant gains in some areas, it still has hallucination rates that need to come down before we can apply it confidently to critical parts of the business, including pricing.

As noted above, there is context and expertise that the AI has not yet learned. If an individual with limited pricing expertise were to listen and rely solely on the responses from GenAI they could create significant risk for the business. They wouldn’t know how to question the responses the AI provides or be able to push it, question it, know when it’s missed the mark or know how to redirect it.  All are critically important to getting to the right, valid answer, not the fake news, click-bait headline that often gets returned in the first response.  

In our case, LevelSetter is using machine learning and our algorithms continue to learn and improve. Could we put an Agent in front of the LevelSetter engine to begin making its knowledge more accessible to the masses? Possibly, and we might, but not yet.  

Why Choose AI-Augmented Pricing?

Pricing is as much art as science. While AI can process data beyond human scale, it lacks intuition and contextual judgment. AI-augmented pricing empowers companies to:

• Leverage AI’s data-crunching power
• Maintain control with human strategic oversight
• Adapt rapidly while mitigating risk

There are still too many nuances and pricing explorations to hand the keys off just yet.

Platforms like Levelsetter exemplify AI-augmented pricing by combining AI-driven deal intelligence with expert-led review and decision-making—unlocking smarter, more defensible pricing outcomes.

As technology continues to evolve we will look to incorporate different aspects of AI where and when they make sense for our customers and their needs.  While we remain open that there are many opportunities ahead, the hallucination rates of LLMs remain untenable for the accuracy demands of pricing for today’s software companies. 

In the race to transform pricing, embracing AI augmentation—not full automation—is the path to sustainable revenue growth and competitive advantage.