The hype around artificial intelligence (AI) has led to lots of jargon making the technology difficult to understand in product development.
I have asked myself?
What are the elements which product development should cover in the process of building great AI-powered products?
I will answer the question by exploring the elements of the AI technology development process.
Products require tough decisions and a data-driven approach, though the method for making these decisions can diversify. For instance, machines or humans can make the decisions, and the data behind the process can be static or dynamic.
A focus on decision-making removes away the complexities of specific methodologies or the noise of industry jargon. This more general definition helps managers to be more attentive to the challenge space. It removes distractions that can lead product managers to think about solutions too early in the product development process.
Company managers must fully understand the problem. It will make possible to define specifications, align the team of engineers, researchers, and programmers accurately.
Let's start quickly with what product managers' responsibilities are:
If we look at AI as the problem-solving machine, it'll bring a lot to the product manager's job. But first, please let me introduce you to three levels of product managers' journey.
Product managers need to build and execute with the vision in their minds (roadmap requires it). Then, the strategy comes and asks for attention. A strategy is about doing the right things.
Nokia did the right job and pivoted from the rubber company to a cell phone company. At the same time, Microsoft did not do the right thing (tactic) by acquiring Nokia and ignoring iPhones keyless keyboard.
Product managers need to operate 24/7 on the following levels, but AI sits only on one of them:
Artificial intelligence sits on the tactic level in the product development process. It is designed by its nature to solve specific, well defined, data backed up, problems. Shipping AI-powered products or services without an end goal usually brings no value to end-users.
The Utah-based company, Hire-Vue, began using AI in 2014 as a way to help companies sort through video interviews. The team defined a great problem to solve, how to select candidates leveraging video data.
HireVue believes it can be helpful for processing a huge number of people through the interview process quickly and reviewing the video in a consistent way.
Another good example is AirBnB. The company has developed technology that looks at guests’ online “personalities”. When they book a stay, AI technology scores the risk of guests trashing a host’s home. AirBnB technology searches for fake accounts, users who publish alcohol or sex video, etc.
These two examples show a clear problem and the delivered solution. It is a tactic level, which connected to strategy, influences the change the most!
Reach out to Skuza Consulting for thoughtful product strategy and development services.
The widespread discussion on matters like automation has raised some interesting issues about the future of work.
Typical examples that illustrate this point could be self-driving cars, the car-sharing industry, as well as outsourcing business.
So. AI needs people to solve problems, and this is excellent news!
AI can easily automate operational processes, which are time-wasters or manual activities today.
For instance, customer touchpoints and communications can be optimized based on data to increase conversion or reduce churn. One of the most common examples is a repeatable and standardized questions list, called FAQ. The list is simple for AI to understand; the best answers, output, are known already. Then, AI serves as an advanced business intelligence engine because it accelerates productivity and effectiveness for clients.
AI can significantly improve the user experience of products, and services.
Please find four examples below. The table presents examples of how Dell, Zappos, and Google leveraged AI to create higher-value experiences for their customers:
To sum up, here are six ground rules which help product managers in artificial intelligence product development process:
If your firm needs help in reviewing or designing a product roadmap. If you are working on developing a product strategy, reach out to Arek and schedule a complimentary consultation.
Related: 16 AI-powered tools for Product Managers to include in the toolbox
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