Collaboration Strategies for AI Projects
How Collective Human Intelligence Leads to AI Success
According to several recent surveys, about 80% of AI projects fail, and one critical reason is a need for more collaboration in organizations. Individual contributors may outperform, but there are many benefits to bringing different groups together. The diverse expertise in different domains, the enhancement of problem-solving from multiple perspectives, and the shared responsibility of reducing business risks are just a few of the benefits. A small percentage of AI projects have collaboration strategies, and here are a few to consider.
Cross-Functional Teams are a powerful catalyst for innovation and efficiency in AI and machine learning initiatives. They bridge technical complexities with business realities, ensuring that AI projects operate within the guardrails of corporate governance and regulatory requirements and push the boundaries of what's possible. This potential is fascinating, and my experience of seeing it in real life is inspiring. By fostering collaboration across departmental silos, Cross-Functional Teams mitigate risks, enhance decision-making, and ultimately drive the successful integration of AI into core business processes. The magic of AI success usually happens in the middle of an organization.
A formal Data Governance policy plan is the strategic framework for ensuring data assets are properly managed, protected, and leveraged throughout an organization. Failing to prepare for Machine Learning is preparing to fail in AI. Data cleanliness, curation, and prioritization foster a culture of Data Stewardship, allowing Cross-Functional Teams to perform and deliver AI solutions and unlock the full potential of their AI and ML strategies while maintaining trust with stakeholders. People, processes, and technology must all work together to ensure success with AI.
Performance Metrics and Alignment are the compass and map for AI and machine learning initiatives, embodying Yogi Berra's wisdom: "If you don't know where you're going, you might wind up someplace else." In the complex landscape of AI, these quantifiable measures ensure that projects stay on course and deliver tangible business value. For executives, well-crafted metrics and Key Performance Indicators transform vague technological promises into concrete, measurable objectives. They span the spectrum from technical benchmarks like model accuracy to business outcomes like Return on Investment while encompassing crucial ethical and governance standards. By establishing clear, agreed-upon metrics, organizations avoid the pitfall of directionless innovation and instead chart a deliberate path to AI success.
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By John Thomas Foxworthy
M.S. in Data Science from a Top Ten University w/ a 3.80 GPA or the top 5%
Veteran Data Scientist with his first Data Science Model in 2005
Deep Learning Artificial Intelligence Instructor at UCSD Extended Studies
Master Instructor at Caltech’s Center for Technology & Management Education for Artificial Intelligence, Deep Learning, and Machine Learning