This post is part of the AI for Business series
Shedding light on AI from a non-technical business/product/design perspective.
This is post #2 in our AI for Business series.
Artificial Intelligence (AI) isn’t magic, but it can feel that way when used correctly. For businesses, AI’s promise lies in solving real problems—not shoehorning technology where it doesn’t belong. Let’s try to demystify AI’s role in reshaping how businesses operate!
How to use AI:
Focus on the problem & goal
The process of deciding how to harness the power of AI is just like any regular design thinking challenge.
- Start by identifying the problem - what is it that you want to solve? By focusing on the problem you will ensure you are not using AI for the sake of it, but are actually delivering value to the end user.
- Identify your goal - what does success look like?
- Identify your KPIs - by what metrics will success be measured?
Let’s imagine thoughtbot owns a restaurant called thoughtburgers, home to the tastiest and most thought-provoking burgers!
Our restaurant orders too many ingredients for certain dishes and not enough for others in any one week. Given that many ingredients are perishable and taste better fresh, the problem we might want to address is food waste at the end of the week. Our goal might be to reduce the amount of food we throw out each week.
Our KPI to measure success could be to reduce the value of wasted food each week from X to Y. A secondary goal might be to reduce the amount of times we run out of a particular dish. The KPI might be to reduce the number of times a customer requests a dish we no longer have from A to B.
Once you have this framework it is time to look at your process from end to end.
What is your end-to-end process?
To illustrate this step, we’ll continue with our restaurant example. The process for ordering ingredients for the week might look like:
- The restaurant manager consults with the head chef to see if there are any menu changes for the upcoming week, new specials, etc.
- The manager then looks at the bookings on their electronic booking system for the week.
- Based on this information, and the manager’s previous experience, they start to arrange deliveries from suppliers for the upcoming week.
- At the end of the week, they dispose of the unused perishable items.
This is a really simplified version and there are probably lots of other steps involved we don’t know about (thoughtbot doesn’t own a restaurant…yet…). But, no matter the problem you are trying to solve, write out every step in the process. This will allow you to identify the steps that can be improved.
What is AI good at?
Before deciding whether AI can help solve your problem and deliver you to your goal, it is important to understand what AI is good at doing currently. In essence, there are three ways it can assist you:
- Process Automation: Streamlining repetitive tasks like data entry or scheduling.
- Cognitive Insight: Analysing complex datasets to uncover actionable patterns that are difficult or time consuming for a human to spot.
- Cognitive Engagement: Enhancing interactions with customers or employees through tools like chatbots or digital assistants.
Each of these points is related to three distinct forms of AI technology; cognitive robotic process automation (CRPA), machine learning (ML) and natural language processing (NLP) respectively. We will be releasing separate blog posts on each of these technologies in this series in the coming weeks, so don’t worry about the details just yet. For now, all you need to do is see whether any of these three things align with steps in your process.
Could cognitive insight, powered by machine learning, identify patterns in our restaurant customers’ consumption habits that our manager cannot? Almost certainly.
Could our restaurant manager then make more informed decisions about how much food to order each week, reducing our food waste? Highly likely.
Could cognitive robotic process automation handle the restocking for our manager, so that they can spend more time engaging with customers? Probably.
By focusing on the problem, goal and your processes, you can leverage AI initiatives and align them with real business and customer needs.
AI is a tool, not a product:
It’s important to remember that AI is a tool to help you reach your goals, but it is not a product on its own.
Amazon is a great example of a company that gets this right. Amazon doesn’t plaster their homepage with references to “AI-powered this” or “data driven that”. In fact, mentions of the letters “AI” on their homepage are limited to “Hair care”.
But under the hood Amazon is running some incredible AI-powered systems. Their warehouses contain a dizzying number of robots that automate an array of tasks. They use data to provide a personalised experience for individual users interacting with their site. They are even able to identify patterns in user data that allows them to get an order as close as possible to a customer’s address… before the customer actually clicks buy.
So why don’t they talk about this more? Why don’t they shout about their really cool tech to their customers? Because their customers don’t care. They don’t care that a robot transferred their package across an enormous warehouse with incredible precision, or that the package they were thinking about buying was already in transit to a hub near them.
What customers care about is that when they buy something it shows up at their door quickly, often within a couple of hours. That is real customer value and providing it helps Amazon get closer to their goal of being Earth’s most customer-centric company.
Many companies fall into the trap of branding their products as AI driven in the expectation that this will wow customers. But, if it doesn’t provide real value, customers won’t care and the company won’t succeed.
(Maybe) don’t buy into the hype?
Like electricity, AI’s long-term impact could far exceed our current expectations, but it’s a matter of time and refinement.
AI has the potential to be a game changer for businesses, but the pace of change is more likely to be an evolution rather than a revolution. Consider self-driving cars. They’ve been grabbing headlines for over a decade now. While the technology exists, they haven’t yet revolutionised our daily commutes. So while exciting times and opportunities are certainly here, refinement is needed.
AI is not a magic wand; it’s a partner in solving specific business challenges. Use it where it genuinely adds value and fits into your broader strategy. By focusing on problems and goals, treating AI as a tool, and keeping an eye on both current and future opportunities, businesses can avoid the pitfalls of hype and make the most of this transformative technology.
💡 If you’re ready to start using AI to transform your business, thoughtbot would love to work with you. Let’s talk about making your AI initiative a success!
This blog post is part of a series based on the course Artificial Intelligence (AI) for Business run by University College Dublin (UCD). I took this course and found it so helpful that I’m recapping my top insights. thoughbot has no affiliation with UCD.