“We should really do something with AI.” Such phrases are not uncommon in boardrooms. And although the intentions of the people making those claims are - generally - positive, we see that the momentum within companies to dive into the realm of AI is often triggered by “The fear of missing out.” And frankly, it’s rather tempting to dive head-first into a promising AI use case although it’s usually not the wisest approach. Let's talk about how to identify the right AI use case for your company.
AI is not 'just another hype', but the hype factor surrounding AI is real.
The past has taught is that following a technology-first approach may lead your organization to one or more of the following pitfalls:
If your AI project lacks direction and is not fully aligned with the company objectives, chances are that your investment project might result in wasted resources and inefficiency. Ask yourself: ‘Is it clear which problem(s) our AI idea actually solves?”
Collaborating on innovation projects can be intensive. Asking your employees to interrupt their operational tasks without this resulting in clear benefits may lead to them getting confused. Or even frustrated.
If the added value for the people that need to use the solution is not clear, you can't expect them to embrace it. Consider engaging your target audience (and especially your early adopters) early on, as they can often provide invaluable insights.
Without clear priorities it’s difficult to ensure that budget and resources go to where they are most needed, when they are most needed. Without clear priorities, you may expect consistent delays and bloated budgets to be common.
Data is one of the key resources for AI applications. Starting with the wrong hypothesis may lead to big challenges trying to include the right data in your models. The worst case scenario? Missing key input data in your initial analysis may result in having to rebuild your data model from the ground up in a later phase.
Often overlooked, but possibly the cause of the biggest challenges for AI projects... Many AI applications deal with personal data, financial data and other sensitive data. Overlooking the requirements from a legal, ethical and/or regulatory perspective may risk blocking the roll-out of your AI solution after it has been developed.
When it comes to technology innovation, people have the tendency to put the cart before the horse. That's a risk leading them into doing “technology for technology’s sake”. This is no different when it comes to AI.
You might be asking yourself: “If technology is not the starting point to spot AI use cases in my company, then where should I start?”
Well… you’ve started asking questions. And that’s a great starting point to find interesting use cases. Our advice is to dive into the problem space. Ask questions first, shoot later.
If I had an hour to solve a problem I'd spend 55 minutes thinking about the problem and five minutes thinking about solutions.Albert Einstein
One effective way to kickstart your journey into the world of AI use cases is by employing the power of "How might we..." questions. These open-ended queries encourage creative thinking and problem exploration. Instead of immediately jumping to solutions, start by framing the problem in the context of your business. For example, "How might we improve customer engagement in our e-commerce platform?"
This approach encourages your team to brainstorm and delve deeper into the challenges you face, providing valuable insights on where AI can make a significant impact. Einstein's words hold true: spend ample time understanding the problem, and the solutions will follow more naturally.
The AI canvas is a highly useful tool to validate AI use cases from various angles. It begins by guiding organizations to understand the problem they want to solve and clarifies the desired outcomes. Through its structured approach, the canvas prompts consideration of factors like user adoption, financial implications, potential barriers, and - very importantly - alternative solutions. By leveraging the AI canvas, businesses gain a comprehensive view of their AI initiatives, ensuring that they are well-grounded and aligned with their overarching goals.
The canvas is flexible. It can be used by an individual to quickly validate an idea, but it can just as well be used as a collaboration tool for an innovation team to comprehensively elaborate an AI use case. In both cases, tools such as the AI canvas can help you to systematically validate your AI use cases, identify potential risks and set realistic expectations on the value you may expect once you start building.
What’s next once you have identified and validated a solid AI use case? In order to start the successful development of your AI solution, we recommend adopting an innovation framework that aligns with your organization's goals. Design thinking, lean product management, and agile development are three powerful methodologies to consider.
Combining these frameworks can provide a structured approach to AI innovation that keeps your efforts aligned with your business objectives.
In conclusion, while the allure of AI is undeniable, it's crucial to approach it strategically rather than rushing into it due to FOMO. We advise you to focus on the problem space first, using “How might we…” questions. By using the AI canvas, you can identify and validate AI use cases and determine which ideas are worth pursuing. Once you start the development of your idea into an AI based solution, make sure to use an innovation framework to align your efforts with your business goals and maximize the value of your AI investments.
Not sure how to start? We're happy to guide you through the innovation process.
Download The Value Hub AI Canvas here and go ahead!