The AI Monetization Challenge

Artificial intelligence has quickly become embedded in modern business. From software applications and communications platforms to connected devices and enterprise workflows, AI is no longer a standalone capability. It is becoming another component of the products organizations deliver every day.

At our recent Quote-to-Cash Executive Exchange in London, LogiSense CEO Adam Howatson explored what this shift means for technology leaders. His message was straightforward.

The challenge is no longer simply adopting AI.

The challenge is maintaining control over the commercial model that surrounds it.

Watch Adam Howatson's presentation below. 

AI Is Delivering Real Business Value 

There is no shortage of examples demonstrating AI's potential.

Scientific research is accelerating through AI-powered discovery. Restaurants are processing millions of customer interactions using AI-driven ordering systems. Organizations across industries are embedding AI into products, customer experiences, and internal operations.

Many businesses are already seeing measurable improvements in efficiency, customer service, and productivity.

But as organizations move beyond experimentation, a different challenge is emerging.

How do you manage the commercial impact of AI once it becomes part of your core offering?

AI Costs Are Becoming Increasingly Difficult to Control 

Unlike traditional software, AI introduces a consumption layer that most organizations do not directly control.

Every prompt, inference, API call, or generated response consumes resources that are priced by a third-party provider. Those pricing models continue to evolve, often without warning.

Organizations are already experiencing situations where AI consumption unexpectedly exceeds budget because changes in token usage or pricing dramatically increase operating costs. Engineering teams are then forced to pause projects while they investigate why costs suddenly escalated.

This creates a new operational challenge.

If your product depends on AI, your margins increasingly depend on how effectively you manage AI consumption.

Modern Products Are Becoming Composite Products

Perhaps the most important concept Adam introduced is the idea of the composite product.

Today's products rarely consist of a single application or service.

Instead, they combine multiple capabilities delivered by different providers:

• AI services
• Cloud infrastructure
• APIs
• Connectivity
• Third-party data
• Digital content
• Subscription services

Each component contributes value to the overall customer experience, while simultaneously introducing additional costs and commercial complexity.

A connected device, for example, may rely on cloud services, satellite connectivity, mapping services, AI-powered analytics, and mobile applications simultaneously. Each of those services has its own pricing model, cost structure, and commercial relationship.

As more organizations embed AI into their offerings, this complexity only increases.

AI Should Be Managed Like Any Other Input Cost

Many organizations still think about AI as a feature.

A more effective approach is to think of AI as an operational input.

Just as manufacturers monitor raw material costs, organizations building AI-enabled products must understand:

• How much AI each customer consumes
• Which AI models are being used
• The cost associated with every interaction
• The profitability of every product and customer

Without this visibility, AI can quietly erode margins while usage continues to grow.

Commercial success depends not only on delivering AI capabilities but also on ensuring they remain economically sustainable.

Flexibility Will Become a Competitive Advantage

One of the greatest risks organizations face is becoming commercially dependent on a single AI provider.

Large language model vendors continue to evolve their pricing, token structures, capabilities, and commercial policies. If your business cannot adapt when those changes occur, your pricing strategy becomes constrained by someone else's business decisions.

Organizations need the flexibility to:

• Introduce new pricing models
• Switch AI providers when appropriate
• Bundle AI into broader product offerings
• Apply usage limits and thresholds
• Protect margins while continuing to innovate

The companies that retain this flexibility will be far better positioned as AI continues to evolve.

Monetization Is Becoming a Strategic Capability

The conversation around AI often focuses on models, infrastructure, and technical innovation.

Yet long-term competitive advantage may come from something less visible.

The ability to monetize AI effectively.

Organizations that can accurately measure usage, understand costs, adapt pricing, and protect profitability will be able to innovate with confidence while maintaining control of their commercial outcomes.

As AI becomes embedded in every product and service, monetization is no longer just a billing function.

It becomes a strategic capability that determines whether innovation creates sustainable growth or simply generates higher operating costs.

Watch the Full Presentation

Adam expands on these ideas in his presentation from the Quote-to-Cash Executive Exchange, discussing why AI is transforming modern products into composite products and why organizations must retain commercial control as AI becomes increasingly central to their offerings.

Watch the full presentation above and discover why the future of AI depends as much on monetization strategy as it does on technology.