AI transformation messaging is deafening. As such, we put our heads down and focus on the tactical steps: Build a model, train a model, release a model, watch a model, and optimize the model. Technology reinforces this execution mindset with no-code and low-code AI platforms, feature stores with deployment automation, and machine learning plus operations (MLOps) and data observability tools (the latest AI darlings). While we look into our dark-mode screens to shield our eyes from the AI glare, we miss the real transformation happening in AI. 

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Tap into the kinetic potential of communities to scale AI for bigger results.

AI communities are taking hold. These communities are more than networking zones of the past. Nor are they simply hacker playgrounds and contests. Today’s AI communities come with a capital “C.” They create AI centers of gravity to propel real business and market transformation with outsized results like simulating new business models and linking supply chains.

The kinetic potential of community changes historical partnerships and collaborations to new ecosystems of creativity, innovation, and pragmatic solutions for things like hacking industry with community minds to metaverse experimentation. Community crosses industry and competitive boundaries and forges new relationships with governments and universities. Where consortiums created standards, AI communities define tomorrow’s experiences and value in sophisticated ways, including using AI to enable AI and deploying protected platforms for codevelopment and training.

Not all community activity is the same or enabled in the same way. But there are launchpads for business stakeholders and IT organizations to capitalize on today: 

  • Shift from center of excellence to community of practice. Business efforts, capabilities, talent, and technology advance AI when coordination and orchestration is encouraged and reinforced. For one energy company, establishing internal data communities in practice and reinforcing them with social portals was the answer. Four social portal themes let data engineers, cloud teams, data scientists, database developers, and data stewards enable their data-driven agendas. These zones provide central points for projects, collaboration, communication, idea and support exchange, training, external developer community access, and architecture and asset libraries.
  • Migrate AI sharing to community exchange. AI needs a combination of data and algorithms available beyond the internal walls of the organization for a representative and complete picture of customers and markets. Sourcing quality, relevant, and impactful assets can be a challenge. Exchanges such as Explorium are tackling the intersection of machine learning and data by assessing the ML model and suggesting better data and features for training and optimization. Other AI exchanges offered by companies such as Infosys, KPMG, and PwC provide AI components and networks to build and test machine learning capabilities.
  • Tap into AI community model development platforms. Having a set of expert third eyes on a newly developed model improves the model output and potentially improves AI stewardship. HPE’s Swarm Learning platform gives data scientists a secure platform to exchange and train parameter-based models without exchanging data. Using blockchain, the platform controls and audits models submitted for review, training, changes, and optimization by an approved community of data science members.
  • Disrupt the market with community subject matter experts. Industry disruption from AI is not only a startup skill. Companies such as Volvo and Ford partner with tech giants like Google to reimagine cars and fleets with connected, self-driving, and electric vehicle technology with AI at the core for next-generation transportation. Medical device manufacturer MEDITECH partners with insurance companies to capture claims related to device defects and provide a frictionless and automated way to reimburse their customers. Whether AI is primary to or a core capability of a partnership, the voracious need for new and hard-to-uncover insights creates a natural pathway for counterintuitive or novel sharing and collaboration to emerge.