How do you connect the cool Artificial Intelligence (AI) pilot project to concrete and measurable business value? How about translating your digital-native AI monetization model into sustainable revenue streams? This isn’t easy, especially when you’re first starting out and have to figure out everything from scratch, but it is possible and many others have done it before you.
But hold on a minute: What does it even mean to monetize AI products and services? Does it mean selling software licenses or subscription plans? Or is it something else entirely? And how can you know if your AI project has any value at all, let alone enough value to justify a business model?
As the technological side of AI continues to get commoditized (read: democratized) at a fast pace, the AI magic is no more about that fancy deep learning architecture or production-ready models only. Equally critical, if not more, is to come up with the commercial context and questions in the first place and follow up on solutions and commercialization strategies to ensure business success. Let’s go through this journey on how companies can monetize artificial intelligence, and also consider digital-native models that have already been proven in other industries.
What is AI monetization, anyway?
AI monetization refers to the generation of revenue from an AI project, product, or service. Unlike traditional hardware and software products, which can be sold as a one-time purchase for a flat fee with multi-tiered service and support plans, AI necessitates a more complex model that recognizes its value over time and scales with its usage. In other words, it’s not about selling your technology; it’s about selling what your technology does for your customers over a continuous time horizon.
Types of AI product and service monetization
In order to monetize AI products and services, companies should broadly think about three main types of monetization paths, including data-driven (subscription fees), usage-based, and hybrid models. Each model has its own advantages and disadvantages depending on a company’s position in its AI journey.
- Data-Driven Monetization: The premise behind these models is that they generate revenue by answering a fundamental question on how do you monetize data? This may be achieved via selling access to proprietary datasets or datasets derived from third parties, like an API marketplace or premium dataset subscription service. While not all data is created equal, and cannot be monetized due privacy and security reasons, among others; anonymization and data abstraction models enable this option for many organizations.
- Usage-Based: This type of model focuses on how customers use a product, for example with per minute pricing for cloud computing environments, etc.
- Hybrid Models: These are built on top of one or more other models. For instance, a model could be based on transaction volumes plus subscriptions.
Connect With Business Strategy and ROI and Understand Digital Business Innovation
The fundamental provision towards AI monetization is that companies need a way of measuring how AI is performing, and preparing for the potential of AI to lead to high ROI already on early pilot projects. This key requirement necessitates that AI innovation and business goals are aligned from the word go; including domain-specific KPIs, return-on-investment views, etc. To connect and optimize your AI pilots with your business KPIs, create a unified view that takes into account both enterprise needs and technical possibilities. As you’re doing so, consider your company’s business model: Is it based on software subscriptions? Or do you sell hardware products? How will AI play into these models? Which part of your company—the supply chain, customer service, marketing—could benefit most from AI right now? What is its primary use case? Who should own it within your organization: IT or line-of-business executives? Once these are ironed out, the path to monetization will be much clearer later in the AI product journey.
Key Requirements to Turn AI Innovation Into a Revenue Stream
Before you can sell your artificial intelligence innovation, it’s important to meet three basic requirements. First, ask yourself whether you have something that is truly new and of interest — both from the technology and business side; if not, then don’t invest time in selling AI for those particular applications, rather pivot to other use cases at hand. Second, ask what kind of customer you are targeting—is it a B2B or B2C client? Note that a traditional B2B company may enter B2C channels due to the nature of AI services at hand, for example, leasing hardware and charging on consumption level. It is extremely critical to break the traditional boundaries and think about the real target market. Third, ask how much revenue do you want to generate from AI pilot projects? This will help you determine which monetization model will work best for your business. As discussed above, there are several ways to monetize an artificial intelligence project: license fees, consulting fees, monthly subscriptions (SaaS), or one-time payments (hardware). The right way depends on how much value your product delivers and how much effort it takes for customers to adopt it.
Be Customer-Centric and Know Who to Partner With
AI is evolving, and so are customers’ pain points. Which AI applications address these top pain points can be key to achieving product-market fit and gaining customer traction. Customer intelligence should be used in conjunction with market intelligence to ensure you’re focusing on solving a real pain, rather than using AI for marketing purposes – a prevalent practice. In terms of partnerships, you can use your market intelligence to identify target companies that would benefit from your technology or services—and vice versa. This type of collaboration could help both parties realize greater value than they could have by working alone. Note that these are ecosystem partnerships, both within and outside your particular industry, in contrast to traditional seller-consumer relationships.
Look Inward: AI-driven Functionality Enhancement of Your Existing Portfolio
Those who master any type of product or service monetization understand that revenue isn’t born out of thin air. The AI monetization journey begins with looking inward at an organization’s existing product portfolio. Are there opportunities for AI-driven functionality enhancement? If so, how can these be leveraged to generate additional revenue streams? For example, your company might have a flagship high-capacity router or a mobile app that generates $10 million in annual revenues. Could these be enhanced through deep learning-based traffic optimization and augmented reality (AR) technologies, respectively? How about using natural language processing (NLP) and machine learning (ML) for predictive customer service chatbots? Net-net, look for these opportunities within the organization, as opposed to going for the latest and greatest AI applications.
Look Outwards: Create AI Service Wrapper Around Your Products
While AI products can be sold as a product, look for ways to build value-add services on top of your legacy products and sell AI as a service. It’s a way of building on what you already have and can present some additional sales channels. Your business partners may also help you monetize your AI product by suggesting services they’d like your AI platform to offer in order to make it easier for them.
Co-Develop and Iterate with Your Ideal Customers
The inward and outward approaches can be summarized into three basic categories: create a new product or service, embed intelligence into your existing product or service, or upsell. For each of these approaches, it is imperative to co-develop with your clients and iterate based on feedback to validate that you’re creating actual business value. As data-driven businesses are quickly becoming more common and frequent, AI products and services are developed iteratively and on real-world data. The question on how to monetize AI must take into account market needs, technology advances, as well as business model integration and evolution —there’s no such thing as a final version of any product or service.
This will help ensure that you can successfully sell your solution. Your customers may have specific needs around data collection, data privacy, or other factors—and it’s important to understand what they need so you can provide a solution that actually works for them. This is also a great way to learn how people are using your product in their day-to-day lives—which could lead to future ideas for additional products and services down the line, and evaluate if AI is a profitable business for your organization.
Identify AI Product and Services-Specific Pricing Models
Beyond simply selecting make or buy, companies also need to consider how they can break down their AI projects into smaller services and deliverables. Breaking a project down into smaller components—and then separately pricing each component—allows clients to buy just what they need from your company. For example, one company may want to train its own neural networks for natural language processing; another may want you to do it for them. Either way, there’s an opportunity for you to create an add-on service that allows both of these clients to pay only for what they use in order to fit within their budgets. This is also a great option if you don’t have enough capacity on your own team: You can outsource some work while keeping other work in-house.
Marketing, Launch and Post-launch Recurring Monetization of AI Products and Services
As with any marketing campaign, you need to make sure that people know about your ground-breaking AI product or service. This necessitates omnichannel engagements with a clear focus on early sign-ups —the more organic adoptor, the more resources you’ll have available to spend on customer acquisition later. The key is finding a balance between spending too much up front and not having enough money later to acquire new customers. It’s important that your early adopters see value in what they’re getting out of your service or product before they start paying for it; otherwise, they won’t stick around long enough to help fuel growth through word-of-mouth marketing.
On the other hand, with AI software products and services, you want to focus on building a recurring revenue stream around your product or service. To do so, you need to make your service or product easily accessible and easy for customers to pay for over time. However, you will also have a limited-time offer in which paying ahead of time can bring about added value.
Final Words
AI monetization, while different from traditional hardware and software products, is not rocket science. Over a long period, these iterative monetization strategies haven’t been available to most industrial players, be it larger organizations or early-stage startups, leading to significant lost business opportunities. The best practices laid out above are aimed at creating more of these types of opportunities for the next generation of industrial companies looking to maximize their revenue potential through AI monetization.
