SVP and chief information and digital officer of Allegion, responsible for worldwide IT and security, data and analytics, and digital projects.
In my most recent post on artificial intelligence (AI), I explored how to lay a solid basis for using AI to achieve your goals. Now that you know how to get started, let’s take a deeper look at the job of the product owner.
The product owner is responsible for giving guidance and priority to the cross-functional AI team, as well as ensuring that everyone remains focused on the overarching vision and road map. This job is in charge of bringing together people with various talents and experiences to work toward a shared objective. Here are four important abilities for any AI product owner:
Effectively Manage Change
Change management is frequently an important aspect of the product owner’s responsibilities. The product owner is responsible for managing change for the business stakeholders, various types of developers, and end-users of an AI product.
The AI product owner frequently needs to manage a transition in mentality from rules-based design to adaptive, machine learning for business stakeholders and engineers (AI-based design thinking). AI-based design thinking, like the principles covered in my recent post on transitioning from project to product management, does not attempt to apply a set of rules for decision-making, but instead generates a continuous cycle of learning and adapts depending on training data.
Furthermore, because humans are frequently closely interwoven into the AI function, good AI design takes into account the human aspect inside the system as well as the individual’s actual usage of the product or service rather than imposing bias through any one person’s preconceptions. Working directly from problem to end solution without conducting adequate user research will almost always result in a bad product.
Building an accurate machine-learning model is just as crucial for end-user acceptance as correctly implementing an AI model. Will the consumer utilize the model if they don’t comprehend what it’s doing? User experience (UX) is critical for understanding end-user expectations and preferences throughout the design process as well as during training after the product is deployed. Trust and user confidence will grow as a result of good training.AI may make users feel intimidated, and engineers are typically acclimated to a different type of design thinking and code (i.e., rules-based system design). Addressing these issues and assisting them in shifting their mentality is critical for effective implementation.
Oversee Multi-Functional Teams
Before we proceed, it is important to realize that not all machine learning is AI. Some machine-learning algorithms, for example, may generate forecasts for decision-making but are not integrated into any program. Instead, these algorithms are employed to power dashboards or give prediction data in reports; the team for that less sophisticated method is different.
True AI systems necessitate a cross-functional team with people from many backgrounds, including web developers, software developers, engineers, business stakeholders, and, of course, UX designers. As a product owner, it is critical to bring everyone together around a similar objective while also providing alignment and directing the team in the correct direction.
Obtain Business Support
AI initiatives are usually expensive, requiring huge expenditures over lengthy periods of time, with no profits received until the project is completed. These initiatives might be challenging to obtain commercial backing and funding for. This implies that communicating the benefits of AI to key business executives is important, and excellent communication and progress monitoring are how AI initiatives win and keep corporate support. Product owners that are business-minded are essential because they can assist draw the dots between AI operation, user advantages, and business outcomes.
To evaluate success, it is critical to first define what success looks like, which can be difficult for business people who are not well-versed in analytics to express. Establishing success metrics early in the project will help to level-set both the AI project team and the business team and establish consistent expectations. Find milestones within the iterative process, assess progress against the goal at each stage, and celebrate achievement along the way after establishing how to judge success.
Effectively Communicate Results
Effective communication is crucial in every endeavor. After the product owner has collaborated with the company to identify success metrics, continual communication of new capabilities and advantages is a critical step toward achieving business commitment and user acceptance. After the project is completed, remain in touch with the company and remember to close the loop and communicate if the desired return on investment was met. This not only addresses whether the targeted outcome was accomplished but also leads to business buy-in for future projects.
There is little doubt that AI product owners have a significant amount of responsibility for ensuring effective implementation. However, by putting these four talents, as well as UX design, at the forefront of development, they will be well on their way to success.