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Decoding the AI Revolution: Monetizing Progress with Consumption Pricing Strategies

The business on AI is going to be a multi-billion-dollar affair, and that’s quite evident in the AI revolution. Generative AI, autonomous agents, and other related innovations will transform companies like Generative AI and usher in the rise of a new generation of GAI-powered local disruptors. But how will this fortune be made? What’s the best way to monetize the value of AI from both the vendor and customer perspectives?

AI is more than just a technical shift. Like the shift to the cloud before it, AI is redefining how we think about creating and using software. Much like the cloud era, the software industry will adapt to its dictates—creating new ways to determine prices, packaging, and business models in the AI revolution. 

It’s imperative that there be an equitable adjustment for both technology providers and users, and we need to start thinking clearly about its implications in the AI revolution.

AI transfers value units from consumers to jobs

It’s not the first time a disruptive technology has forced new thinking in business models. With the rise of cloud and SaaS in the AI revolution, subscription pricing models emerged for their ability to determine costs, provide agility, and obtain the value of continuous updates. For users, there’s more flexibility in cost while achieving better efficiency in the usage of software, seat by seat. For retailers, subscriptions offer recurring revenue rather than a one-time sale of extensive licenses on the subscription site.

In the pre-AI era, licenses for individual seats were the norm. The more people using your product, the more you charge in the AI revolution. However, applying this to AI-driven tools doesn’t work the same way.

There’s a fundamental difference in how you determine costs with or without AI. Without AI, you remain in control of empowering a user to play a role—whether it’s a customer service representative, a packaging manager, or an HR personnel in the AI revolution. With AI, the focus shifts to employment, not people—assigning AI to autonomously resolve support tickets, generate purchase orders, or organize onboarding processes for employees.

With it, the unit of value shifts from consumers to jobs in the AI revolution. This isn’t about an AI agent getting the same salary as a human. That defeats the purpose. Instead, it provides a benchmark. At the top end, an AI agent should represent significant savings as an employee or consultant. At the lower end, the vendor needs to fulfill the cost of providing the service (a figure that will likely decrease rapidly under Moore’s Law) with a reasonable margin for profits.

For AI-driven solutions, pricing models need to evolve

While many companies already have an estimate of what it costs to handle various types of support tickets in the AI revolution, the application of an agent earning approximately $16 per hour solving a level 1 ticket in about 15 minutes costs $4. A level 2 ticket goes to an agent earning a higher fee and may take a bit more time. There’s an additional leap in cost at level 3.

AI-driven solutions for pricing models
AI-driven solutions for pricing models

If an AI agent can handle these tickets instantaneously, that’s $4 saved for each level 1 ticket, and similarly for higher-level tickets. If you’re charging $1 per ticket for this AI agent, you’re moving forward in the AI revolution. If the vendor spends 30 cents on the computer to run it, they’re still ahead. It’s a win-win situation for both sides.

As it appears, we’ll see different price points for AI agents with different capabilities or skills. Customer support AI agents with a higher rate of ticket resolution may come with a premium. A simple scheduling agent might be affordable, creating data visualizations and reports. Companies will pay for higher proficiency and value as human workers do.

Once a model for basic pricing is clearly stated and understood for AI, packaging models like bundling can be introduced. A company knowing it has at least 10,000 support inquiries per month in the AI revolution can prepay for that volume, benefitting from reduced rates and then adjusting for any excess over the normal rate.

Change in cost structures won’t come easy, and not everyone will navigate it successfully. However, the benefits will be there. While the cloud may have eliminated the revenue model of on-premise software, it also significantly expanded opportunities for the industry and global economy in the AI revolution.

AI will have a similar impact. Users will pay less and gain more, while technology providers will harness the broad capabilities of this technology to deliver services. In navigating the business models of the AI era, upgrading and adapting to the Rolling Forward Average (R4HA) system in the AI revolution and well-established corrective techniques for cost sustainability will be essential.

Navigating Advancement in AI-Era Business Models

For local businesses, determining the value in their business model using the AI Revolution is a relatively straightforward affair. However, for existing SaaS vendors, it’s not that simple. When your entire operating model is based on set prices, navigating through different perspectives, from your P/L to the mindset of your salespeople, can be quite challenging. This is especially true when considering the need to eliminate your current annual recurring revenue.

In the face of this challenge, it’s understandable that some SaaS companies have introduced new AI features with per-seat pricing, perhaps hoping to fulfill the expectation of reducing the number of users by charging more for each. It remains to be seen how sustainable this viewpoint is in the competitive AI Revolution market.

Businesses centered around value will need to strategize how to determine pricing, including bundling for users, product identification, and managing sales commissions. New businesses are already being established to provide metered or seat-based pricing as a service.

Changing pricing structures won’t be easy, and not everyone will navigate it successfully. However, the benefits are there to be reaped. While the cloud may have phased out the revenue model of on-premises software, it has also significantly expanded opportunities for the industry and the global economy.

AI is expected to have a similar impact. Users will achieve more with less, and those providing services will harness the extensive capabilities of this technology.

For More Info: AI Hardware

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