AI-driven technology continues to expand in all business sectors, allowing faster and more insightful data analysis on internal processes, product development, and customer behaviors and preferences. AI is increasingly useful for product managers when applied to product development teams and continuous process improvement.
The adoption of AI is accelerating. A recent McKinsey & Co. “State of AI” report found that 56 percent of respondents reported their business had adopted AI for at least one business function. They also reported that the impact of AI on the bottom line is growing.
Uses of AI and Data in Business
Product management combines engineering and project management skills to lead product development teams. The consumer product industry first created the position to oversee the development and production of everyday products. The software industry has adopted product management principles to lead software tool development. Students in a graduate engineering management program with a concentration in project management prepare themselves for leadership roles in the field.
AI already has a significant impact on how product development teams operate and the products they create. For example, according to the State of AI report, AI-based product enhancement is among the most common current applications of AI. Other applications include product feature optimization, the creation of new AI-based products, predictive service and intervention, and risk modeling and analytics.
Businesses also increasingly use AI-driven technology to provide better service to product end users, to which product managers who practice Lean and Agile are well accustomed. Some of the uses of AI in this area include service operations optimization, contact-center automation, customer-service analytics, and customer segmentation.
How AI Can Improve Product Management
Product managers can also use AI-enhanced tools to work alongside their project teams, often using AI to improve product management.
Estimating completion times. AI-enhanced technology can more accurately predict completion dates through deeper data analysis, eliminating managers’ need for “rough” estimates.
Improved tools. AI-driven technologies offer more user-friendly digital tools to manage issues such as extensive document searches and real-time data analytics.
Routine tasks. AI technology can manage administrative tasks, freeing managers from more creative work. AI can also improve some decision-making tasks.
Risk assessment. More robust data analysis leads to better detection and faster analysis of potential risks, such as project delays or additional costs.
Challenges in AI Adoption
AI-driven technology shows promise in both improved products and project team operations. However, there are hurdles to overcome.
Vince Stuntebeck, co-founder and the CEO of ZAnalytics, suggests that challenges include a “build vs. maintain” mentality, redundant AI models proliferated across an enterprise, and a lack of clarity around enterprise ownership of data and AI products.
Incorporating the Boston Consulting Group growth-share matrix helps product managers create a sound development strategy. This planning tool allows businesses to visually represent their products and services, helping drive decisions on what to keep, sell, or invest more capital. Other helpful strategies include developing a product management training plan and investing in creating a product catalog that supports road maps.
Students who enter Merrimack College’s online MS and graduate certificate programs in product management learn the latest uses of AI in product management from a faculty of experienced professionals. Graduates emerge prepared to lead talented development teams creating cutting-edge products that enhance and improve our lives.
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This post was originally published by Merrimack College.
Jeff Hovis is the Program Director, Graduate Product Management Program at Merrimack College.
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