Many companies have initiated projects in artificial intelligence (AI). They are experimenting with the technology and are trying to find ways to integrate it into their products, services or internal processes. While non-AI initiatives are under pressure to justify expenses, AI related projects are easy to fund. “AI or die” seems to be the motto. But which of these initiatives will be game-changers and turn into profitable products and businesses? Which will significantly reduce costs? Will the resources spent on AI bring a ROI? These are difficult questions to answer and - given the AI hype - often not asked.
Let’s have a look at the key underlying reason why it is hard to predict the success of AI projects: Companies are focusing on the technology without being clear on what customer problem they want to solve. They are putting the cart before the horse.
Let’s make some examples: A leading job platform uses AI to enable job candidates to generate CVs faster. Sounds good, but is speed really a problem when creating a CV? A tax consultancy with more than 3000 consultants helping average income people to fill out tax forms is considering an AI-based platform to enable their clients to fill out the tax declaration automatically. Do the customers really want less effort to fill out the declaration or is it more important to avoid mistakes and prevent getting into trouble with tax authorities?
To us, the AI rush feels like the early stage of the internet (“we need a website”) or mobile (“let’s build a mobile app”).
The challenge of every new technology for customer-facing projects is this: Does it provide so much more value that users or customers will adopt it, i.e. replace their existing way of solving the problems with a new way? From a technology perspective, AI can be integrated into a lot of aspects of your business – but where does it really provide more value? To answer this question the customer and her problems needs to be well understood first, before we can answer the question that an AI solution is worth the development effort. Does it really solve a problem big enough to be solved?
This is where Jobs-to-be-done and our Customer-Focused Innovation process comes in. In a nutshell, CFI measures customer Pain Points first and then spins solution concepts towards these opportunities. It first asks independently of any solution: What is the Job-to-be-done that potential users of an AI solution are trying to achieve and what difficulties occur when trying to reach the goal? Jobs-to-be-done are goals, tasks or purposes that humans want to achieve and they use products to get there.
For industrial plant managers the Job might be “to operate a production line” and the difficulty might be “to know as quickly as possible why the line has shut down”. A typical problem AI can potentially solve. For an employee the Job might be “to find a new employment”, with the difficulty “to be sure my application is accepted”. The AI-based CV generator needs to increase likelihood of success, not speed up the writing process.
Hence, the – sometimes literally – million dollar question is: What do new technologies and solutions need to offer, so that customers will want to use and buy it to get their Job done?
CFI measures quantitatively how a Job-to-be-done is solved in a given market today in order to predict the adoption of the solutions of tomorrow. In this thinking, the Pain Points of today are the revenues of tomorrow. The quantitative measuring is done via direct customer explorations to build up a precise, granular customer metric system that can be quantified. As an outcome, CFI provides fact-based opportunities in the form of a Value Map, showing and prioritizing customers’ Pain Points.
Let’s make an example. Imagine being a company providing software solutions to industrial plants. The AI integration possibilities are endless: Should it enable predictive maintenance, enhance data analysis, detect potential risks for production, scan the plant for potential safety risks? Are all of these equally important? Where to start? The CFI Value Map answers that question precisely. It will tell you exactly which of these topics are more relevant to users. And it will give you a priority list where to start and focus your solution on: with the least fulfilled metric. As a result, you will know that the customers will reward these efforts and are more likely to pay a premium and adopt a solution. Because it solves their most burning problems.
Knowing the customer problem early and in a fact- and data-based way will enable companies to precisely aim AI initiatives at Pain Points that are not yet solved in the market. That way you are not looking for problems with a solution, but are able to spin solutions towards opportunities before developing. This makes sure that resources are not spent in a focused way. The result is a customer-focused AI strategy with a significantly increased chance to create value – for the customer and the business.