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The price is right (?) Or how to monetise on your fantastic AI product?

So… you are in the process of creating an AI product that will become a smashing success? Congratulations! Many exciting, frustrating, long hours of development lie ahead.

The great news is that the recent boom in companies adopting and incorporating AI technologies into their daily processes on a wider scale has created an almost insatiable demand for various AI solutions. And one of the greatest advantages of a product company is its ability to create predictable recurring revenues that will drive the value of the company, especially, in the low interest rate environment where future profits do not to be too heavily discounted.

What pricing models should you adopt to be successful in the medium run and ensure that your product flies off the shelves? Unfortunately, there is no fit-all answer. The choice of a correct model is not always a given.

Consider a couple of options on how to charge your clients for your (give yourself a pat on the shoulder) great product – a fixed and a floating fee. Each comes with its pros and cons.

A fixed monthly fee could be a great option. The upside of a fixed fee is that it will allow for better budgeting for both you and your client. On the downside, a fixed monthly fee might not suit all clients. They could be reluctant to pay fixed fees, especially if they foresee a varying degree of usage of the product.

A varying fee could be a solution to the latter. Such a fee can come in many shapes and forms. Here are a few that could be worth mentioning:

  • Outcome-based – agree with your client on the final deliverables in advance and charge once those are successfully delivered.

  • Revenue share – imagine if you could share the extra revenue or profit that your product is creating for the client. The expression “We are in the same boat” would take on a completely different meaning.

  • Per insight – at the end of the day, this is what an AI product creates most of the times, insights. Why not charge for those?

  • Per data point – all algorithms need data to educate themselves. The more data they consume, the smarter they become. Hence adding value to the customer. Most people are willing to spend money on educating their young, right?

Agreeably, these might not be easy to construct and quantify in a fair way. We are not going to go into an in-depth discussion of the above-mentioned models today. The list is not complete and could go on. Instead, we’ll leave you with another question: “What if the clients could trade their data and insights with each other in a safe, privacy preserving way?” Data in the new “Industry 4.0” world is a commodity, as oil was under the “old” economic order. Any commodity can be traded…

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