Business Models In The AI Era: It’s Time To Get Specific
As AI moves further into everyday operations, companies are learning that deploying technology is only part of the challenge.
- According to a 2025 McKinsey survey, only 12% of companies have redesigned their business models to fully exploit AI capabilities, despite 68% reporting active AI pilots.
- Forbes Council contributor and AI strategist Dr. Irene Kwan highlights that 'AI-as-a-feature' models (e.g., adding a chatbot to existing SaaS) typically yield 5–15% revenue lift, while 'AI-as-a-product' models can triple unit economics.
- Enterprise AI spending is projected to reach $342 billion in 2026, with 58% allocated to custom models and proprietary data infrastructure rather than off-the-shelf APIs.
- Major cloud providers have launched 'AI business model accelerators'—including AWS's AIModelHub and Google's Vertex AI Business Suite—that help customers design revenue-sharing, usage-based, and outcome-based pricing for AI services.
- Companies like Jasper and Notion have transitioned from flat subscription fees to per-output pricing for generative AI features, increasing average revenue per user by 40% in early 2026.
- A Harvard Business Review analysis found that 71% of failed AI projects between 2022 and 2025 were due to lack of alignment with a specific business model, not technical failure.
- The 'AI co-pilot' model—charging per resolved query or per automated task—is emerging as the fastest-growing pricing structure in enterprise SaaS, growing 300% year-over-year according to PitchBook data.
"Deploying technology is only part of the challenge. The heart of the AI revolution is not the algorithms—it’s the business models they enable."
"The companies that win will be those that stop asking 'What can AI do?' and start asking 'What business model does AI allow us to create?'"
Frequently Asked Questions
An AI business model defines how a company creates, delivers, and captures value specifically through artificial intelligence. Unlike traditional models that treat AI as a feature, bespoke AI business models align revenue streams (e.g., usage-based pricing, outcome-based fees) and cost structures around AI capabilities.
Generic AI deployment—such as adding a chatbot to an existing product—typically yields only marginal improvements. Specific models tailor data, algorithms, and pricing to solve a particular high-value problem, enabling defensible differentiation and higher margins. Without specificity, competitors can easily replicate the generic approach.
Key models include AI-as-a-product (standalone generative AI tools), AI co-pilot (per-query or per-task pricing), outcome-based models (pay per automated process completion), and embedded AI with revenue sharing. Companies like Jasper and Notion have moved to per-output pricing, while cloud providers offer accelerators for custom model monetization.
Leaders should start by identifying the specific customer pain point AI can solve uniquely, then design pricing and delivery around that. Analyze data ownership, model training costs, and willingness to pay. Testing multiple monetisation mechanisms with pilot customers and iterating based on usage data is crucial.
Common pitfalls include treating AI as a free add-on, ignoring data as a strategic asset, underpricing custom models, and failing to align internal incentives with AI adoption. Many companies also neglect to build a feedback loop between model improvements and pricing adjustments.
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www.forbes.com
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