Artificial intelligence is changing software faster than any technology we've seen in decades. While much of the conversation focuses on AI capabilities, an equally important challenge is emerging: how should companies monetize AI?
Should AI features be included in existing subscriptions? Should customers pay based on usage? Should pricing be tied to business outcomes? And how do software companies balance customer value with the rising cost of AI infrastructure?
These were some of the questions we explored when we recently welcomed Kareem El Muslemany, Pricing Leader for AI and Developer Products at Zoom, to the LogiSense Podcast. Drawing on his experience across Zoom, Microsoft, LinkedIn, and management consulting, Kareem shared his perspective on how software companies are rethinking pricing in the AI era.
The conversation reinforced an important reality: AI isn't just changing products. It's changing how software companies generate revenue.
Traditional SaaS pricing was relatively simple.
Customers purchased licenses based on seats, users, or feature packages. Revenue was predictable, customers understood what they were buying, and software usage had natural limits.
AI changes those assumptions.
Instead of simply helping users perform tasks, AI increasingly performs the work itself. A single user can now summarize meetings, generate documents, write code, analyze data, and automate workflows at a scale that wasn't previously possible.
That creates a significant gap in customer value.
Some users might rely on AI occasionally. Others may use it hundreds of times every day.
Charging both customers exactly the same quickly becomes difficult to justify.
One of the strongest themes from our discussion was the growing adoption of hybrid pricing models.
Rather than abandoning subscriptions, many software companies are combining predictable recurring revenue with consumption-based pricing for AI capabilities.
Zoom provides a practical example. Its newer AI offerings combine traditional seat licensing with AI credits that customers consume as they perform AI-powered tasks.
This approach offers advantages for both software providers and customers.
Customers continue to benefit from predictable subscriptions while paying proportionally for advanced AI capabilities they actually use. Vendors, meanwhile, can better align revenue with AI infrastructure costs while protecting margins as usage increases.
For many organizations, hybrid pricing offers the best balance between simplicity and flexibility.
Consumption pricing often sounds attractive because customers only pay for what they use.
However, usage alone isn't enough.
Finance teams still need predictable budgets.
Procurement teams still want confidence that invoices won't unexpectedly double because employees experimented with new AI features.
As Kareem explained during the podcast, companies introducing AI pricing need to provide visibility before customers receive the invoice.
Usage dashboards.
Forecasting tools.
Credit balances.
Alerts and spending controls.
These capabilities help customers understand their consumption before costs become a surprise.
The objective isn't simply usage-based pricing.
It's building trust.
Outcome-based pricing has become one of the hottest topics in AI monetization.
The concept is compelling.
Instead of charging customers for tokens or API calls, businesses charge for completed work or measurable business outcomes.
While that works well for some applications, it isn't universally applicable.
Support ticket resolution is relatively easy to measure.
Generating qualified sales leads may also fit naturally into an outcome-based model.
But what about creating a presentation?
Summarizing a meeting?
Helping a product manager brainstorm new ideas?
The business value is real, but it's much harder to measure consistently.
In many cases, pricing around meaningful customer actions rather than raw technical metrics may offer a more practical solution.
Customers understand "reports generated" or "images created" far more easily than millions of AI tokens.
During the discussion, Kareem drew an interesting comparison between today's AI market and the early days of cloud computing.
When organizations first moved workloads to AWS and Azure, many experienced unpredictable monthly bills and rapidly changing infrastructure costs.
Over time, cloud providers introduced better visibility, improved cost management, and significantly lower infrastructure costs.
AI appears to be following the same path.
Today's expensive AI models will eventually become more efficient.
Costs will continue to decline.
As that happens, competitive advantage will shift away from access to AI itself and toward the customer experiences companies build on top of it.
Perhaps the most valuable insight from our conversation was also the simplest.
Nobody knows exactly what AI pricing will look like two years from now.
The market is evolving too quickly.
Rather than building rigid pricing models around today's assumptions, organizations should invest in monetization platforms that support continuous experimentation.
New pricing models.
New packaging.
New usage metrics.
New customer offers.
The businesses that adapt fastest will be better positioned than those trying to predict a single "perfect" pricing strategy.
Our conversation with Kareem El Muslemany offers practical insights for product, finance, pricing, and revenue leaders navigating AI monetization. Whether you're evaluating subscriptions, hybrid pricing, consumption models, or outcome-based pricing, you'll gain a deeper understanding of how leading software companies are approaching one of today's biggest business challenges.
Watch the full podcast to hear Kareem's perspectives and discover why flexibility, predictability, and customer value are becoming the foundation of successful AI pricing strategies.