7. November 2018No Comments

How to discover machine learning opportunities in your product

Artificial intelligence (AI) brings the new wave of automation. It changes every business as electricity did a few years ago. Things become smarter and every business is looking for ways to put machine learning into production to bring value to customers. Everyone wants to incorporate that intelligence into their product. But, where to start?

People hiring products

Let's take a step back and think about basic human-tool interaction. We use products to enhance our own capabilities, become more effective, nicer, and better people. We are hiring products to do our tasks. Remember the last time you were at a party, and wanted to go home? That need was the same for the people living 100 years ago. The difference is that now you have more options to do that. Technology is working for us, and we are good at hiring it.

Products are constantly changing, human needs remain the same.

Capture the needs with a job stories

Intercom previously introduced a framework called Job Stories. It's a creative framework to help frame and capture human needs so that you can work with it later in the development process. It has its own pros and cons yet, I find it the most effective way to think about AI automation. While its focusing on targeting human needs, it opens the space for possible technological solutions. And this is exactly the space of AI disruption.

This is the formula for the job story

When (situation)
I want (motivation),
so that (expected outcome).

Try to write down the job story of your product. What job does your product help people to accomplish?

When ___
I want ___,
so that ___.

One product could cover multiple job stories. Somebody gets a car to save commute time, so they can spend more time with their families while others enjoy racing through the country during the weekends. People are going to use your product it to fulfil their needs. Talk to the people and understand for what jobs are they using your product, before designing solutions.

Computer competence growing with Machine Learning

Ok, now it's the exciting part, where Machine Learning comes into play. The ability of machines to learn independently increases its competence. AI can now accomplish tasks that humans can do in 1 second (this is not true, but it helps to imagine the impact it has)

What Machine Learning Can Do

What Artificial Intelligence Can and Can’t Do Right Now https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now

 

Computers can now understand audio, can see and tell what's in the pictures which allows it to do more complex jobs previously only exclusive to humans. In other words, computers can do more complex and harder jobs.

Read A few tips on where to put the AI into your product

Discover opportunities for your product

Knowing what jobs that your customers hiring for, you can start a discussion about possible solutions the ML can bring to the table. Draw 2 axis. The impact it have on the User Impact, and Machine Learning (ML) dependency.

Let's take a look at solutions we did for the educational application. Our goal was to find core functionality for the product using ML to deliver core value of the app. Since the product has not history we formed hypothetical job stories. Using hypothetical scenarios you can start working right now and look for the validation on the way. If you are willing to get it right, use some of the research techniques to validate your job stories before doing so.

Put the job story you want to focus into centre and write down all ideas on possible solutions. Now go through all possible solutions targeting this job, discuss it and place it on the matrix. After few repetitions you can get something like this.

While doing so we found 2 surprising things. The solution we wanted to build fell into fourth quadrant. That area of low user impact and low ML dependency is a no-go as the core feature of your app. Also, we discovered a new solution in the second quadrant, which we hadn't thought of before. When looking to use ML as your core product functionality, that's your spot. Repeat with more solutions and job stories, until you have found the right solution you want to build.

Using this method you can help frame an early discussion with ML engineers and narrow down value discovery discussion. It is also helpful to declutter discussion about cost heavy machine learning solutions, and discovering easy-to-implement alternatives. Share your thoughts in the comments.

Watch the talk on Designing AI Products at UX Monday (in Slovak)

14. June 2017Comments are off for this post.

Show me your apps and I’ll tell you who you are

In 2011 University study suggested people check their phones 30-40 times per day. The experiment showed my average daily pickups 29, spent time 2 hours and 8 minutes. However, the recent study says US customers are using smartphone 5 hours per day. Spending one-third of the wake-up time interacting with technology is astonishing. Pulling out the device and looking for engaging information is a big part of our lives. People working on the digital products holds power similar to atomic energy - it can be used for destroying or powering cities. 

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19. April 2017Comments are off for this post.

How to design useful products

Three years ago I was starting e-shop selling teas. I saw an opportunity to promote its boost effect and sell it to the various workplaces. We have spent half a year building product, developing e-shop and branding. After a launch, we have sent four bags of tea. It was devastating. What did we wrong? Was that positioning of a product, or product itself? Besides looking for ways to improve, we decided to focus on other activities and quit.

Every business owner knows, doing things people need is the only way to success. But how to make sure people won't reject your idea? Is there any way to design a product for success? Human Centered Design looks like a promising answer.

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