7 ways to revolutionize collaborative AI
Has artificial intelligence finally come into its own? It’s true that companies are finally taking AI seriously. But here’s the rub: Clients drink the AI-is-coming-for-you Kool-Aid and want to move fast — faster than the capabilities of technologists and designers to help them. Artificial intelligence isn’t an add-on, it’s something you build into things. Here are 7 ways to revolutionize AI.
Make AI human-centered, not tech centered.
Our goal is to get our clients to flip that technology-led process on its head. Ideally you do research and find the real human needs first, and then you develop the technology. Does AI make your company more or less human-centric? If your focus is people, not revenue, later you’ll reap the financial return.
Improve customer service.
People are impatient. They expect fast results, immediate responses. Do you remember the last time you phoned a bank? You probably got stuck listening to hold music. Use AI to give the customer a more empathetic experience. Put a service design lens to the issue and it quickly becomes obvious: Can AI help a customer service rep do his or her job? What about that person’s colleagues? In what ways, exactly, can it help businesses improve their interactions with customers? Do we use AI to automate certain processes in the loop or to help the human?
Consider the chatbot.
Chatbots are good at solving simple and common queries, but if all they do is replace the human interaction, that’s no solution. People want to talk to a human, and they want those reps to be empathetic. That’s why AI works best when it plays a role in customer service. What it’s really good at is non-human tasks, such as collecting data about a person and connecting the dots. A well-designed chatbot knows when to hand off that data with helpful suggestions to support customer service reps — and when to just stop the conversation and bring a human into the loop.
Remember to respect data.
The ethics of collecting and using data while providing the best possible user experience can be complex. How comfortable are people about letting their data be collected, and who wouldn’t they mind sharing that information with inside their organization? From the product side, what benefit does a product have to offer in exchange for someone to surrender his or her private data? It’s not always an easy or comfortable mix. Say you’re collecting someone’s personal data about his workplace productivity. Ideally, the data provides meaningful insights for him, but also for the organization. You want transparency of data, but at a level customers find acceptable.
Help AI find a comfortable spot in your workforce.
AI will take some jobs, change others, and create some new ones. What’s more compelling is whether CEOs truly understand how AI is affecting them and whether they have a strategy for the future. Because artificial intelligence will transform business in two key areas: customer experience and automation. On the front end, voice recognition, chatbots, and biometrics are enhancing the interface. On the back end, processes are becoming more efficient through predictive analytics and process automation (for example, automating loan applications across departments). How many companies have a strategy that combines both elements while also adopting a change management mindset within the organization?
Engineers, meet designers. Designers, meet engineers.
AI is not yet being designed from a user-centric point-of-view. Here’s the standard MO: “Hey, we’ve got that chatbot technology, let’s use it, it’ll save us money.” It’s rolled out for the wrong reasons.
Change is underfoot, it’s just not fast-moving. Now we’re starting to see designers working closely with data scientists, and that’s exactly what needs to happen. With hackathons all the rage there’s an eye to taking a more collaborative approach. But designers can’t design AI without consulting experts in writing algorithms. It’s equally true that AI engineers can’t work without designers who understand usability, ethics, and emotional design. It’s coming together, but mainly in blockbuster companies such as Google, Apple, and Amazon, which suck up the top talent from machine learning programs at MIT, Harvard, and Cambridge.
Create empathetic chatbots.
Designing a personality for a chatbot can be difficult, and we have another article about that process. Part of it is timing: ensuring that the chatbot knows when to bring a human into the conversation, and when to bring in the data it gathers. On the simplest level, it happens to be the customer’s birthday, so the chatbot nudges the human, Hey, say happy birthday! That’s where the design bit comes in, understanding data points and adding meaning to them.
The more human you make an AI machine or product, the more natural the interaction becomes. The flip side is that the expectations of the intelligence also increases. What happens now is that something appears to be having a conversation with you, but the technology behind it is actually way behind. If we build empathy into a chatbot however, it can change the way it responds to you, by recognizing emotions in your response and changing its behavior. Maybe it uses humor or softens its tone, or alternatively it feels like switching you to a human immediately, and offers that as an option. The important part is training the AI in the right behavior.
