Why You Need to Maximize Your AI Opportunities
As a product manager, one of the biggest mistakes you can make is to think too narrowly about the possibilities of AI. The technologies that enable AI are advancing rapidly and becoming more and more affordable, so it’s a prime time for companies to start taking advantage.
And yet, despite the promise of AI and its ability to reshape businesses, the full scale of this opportunity is just beginning to unfold and it’s not progressing at the fast pace many assume. Why is that exactly?
At the highest level, it’s a reflection of a failure to rewire the organization to understand the importance of good data and hone their prediction (AI) opportunities, identification and capture skills. Startups that try to integrate data and machine learning are largely dependent on the strength of their AI product managers and data science teams. It’s a product manager’s job to help an organization succeed with predictive analytics and machine learning products.
So how can product managers create the conditions for success with AI? From seeing the value of implementing data intelligence initiatives and fully understanding the AI lifecycle, to using a data canvas while making AI investments and implementing our SOAPTM AI framework, these can all help you to hone in on your AI opportunities and ensure they’re integrated into your company product culture.
It’s All About The Data
Strategically collecting and analyzing analytics and data has never been more important. In fact, these are must-haves, not afterthoughts. They are invaluable not only for counting numbers and building dashboards but for helping predict outcomes and define goals, roadmaps and strategies.
This is why you need to uncover the main value drivers for implementing data intelligence initiatives. But these aren’t like traditional software projects. Data intelligence projects generally have longer timelines, higher costs, and contain a number of moving parts that can sometimes be challenging.
Essentially, it's a bottom-up process where you attempt to solve a problem with the team, get a signal early, and then use it to construct the bigger picture. Most issues with machine learning are solved by understanding and preparing the data better, not by simply picking a more complex model. With that in mind, you should set the vision and direction of the product by knowing your training dataset cold, but also stay vigilant of potential data biases.
Building and Iterating AI/ML Projects
AI is a lifecycle that requires the integration of data, ML models, and the software around it. It covers everything from scoping and designing to building and testing all the way through to deployment — and eventually requires frequent monitoring.
Machine learning (ML) is a complex space where results are achieved through frequent iterations. That’s why building and iterating a machine learning model to achieve a level of accuracy the business expects can be a daunting task. With that in mind, it’s important to set expectations to see whether it makes sense to build an AI or ML project. Even with good data training and clear objective metrics, it can be difficult to reach accuracy levels that are sufficient enough to satisfy end-users.
The product manager must understand the consequences of putting an ML model out into the world. Not only do they need to have an intimate understanding of the data and be aware of any data quality issues and insufficiencies, but they also need to define the fall-back solution in the case of less-than-certain results.
Using a Data Canvas
Product managers need to ensure that data scientists are delivering results in efficient ways so business counterparts can understand, interpret, and use them to learn. This includes everything from the definition of the problem, the coverage and quality of the data set and its analysis, to the presentation of results and the follow-up. This is where a data canvas comes into play to help (see screenshot below). Leaders are much more likely to have a defined process for the assessment and implementation of AI innovation, and a data canvas allows them to be methodical and focused when making AI investments.
This data canvas system helps leadership team members, as well as other members in the field, understand the importance of good data and hone in on their AI prediction opportunities, identification and capture skills. A data canvas’ compelling story helps organizations understand the importance of AI initiatives and how all will benefit from them.
Breaking Down Barriers
While AI initiatives face cultural and organizational barriers, it’s the product manager’s job to help leadership take steps to break down these barriers and effectively capture AI opportunities. They should provide a vision to help them understand why AI is important to the business and how everyone and everything will fit into a new AI-oriented product culture.
That’s why companies must ensure that as many stakeholders as possible have the skills and resources they need to employ advanced data and machine learning approaches and best practices. This is where SOAPTM AI shines — our methodology and product leadership framework for product planning and roadmap prioritization based on principles of product strategy, lean startup, user-centered design, data science and more.
Introducing SOAPTM AI and How We Can Help
When there’s a lack of strategy in your product planning efforts, this can lead to a lack of focus and direction, which means things can get messy really quick. That’s where we at Bain Public come in to assess, cleanse and remove friction from people and processes, making sure everyone is aligned, motivated, inspired, and ready to innovate.
Companies that excel at implementing AI through our SOAP™ AI framework will find themselves at a great advantage in a world where humans and machines working together outperform humans and machines working on their own.
- Looking to reduce your workforce through automating work processes and technology integration?
- Are digitization, robotization, and artificial intelligence raising a glaring employability issue?
- Are you concerned that your engineering team isn’t building the right features?
- Do you have a disagreement about your prioritization methodology, executive involvement, and returns on digital product efforts?
If you want to expand this conversation and explore where Bain Public can help you grow faster, let's set a date in the next few weeks, book your SOAP™ AI demo here.
Thanks to Loren O'Brien-Egesborg for contributing to this article as well as reading drafts and overseeing aspects of its publication. Also, if you have any feedback or criticism about this article, then shoot us an email email@example.com.
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