In a June 21, 2026 piece republished on Brian Solis's site and attributed to Vicki Salemi for The Tribune, Brian Solis defines becoming "infinite" as adopting an intentional mindset where AI both automates and augments work to enable continual reinvention. Solis outlines four adoption stages, using quoted labels and descriptions: "AI Followers", "AI Forward", "AI First", and "AI Native". He said AI augmentation expands human capability and that some organizations make AI the default for routine tasks while reserving humans for judgment and creativity. The article frames these stages as progressive modes of integrating AI into work and product design, with AI Native meaning a product or service that would not exist without AI, per Solis.
What happened
In a June 21, 2026 column published by Vicki Salemi for The Tribune and republished on Brian Solis's site, Solis defined "becoming 'infinite'" as an intentional mindset where AI "will automate and augment work perpetually, to free up time and resources to allow constant expansion and reinvention," said Solis. He outlined four stages of AI adoption and identity, using quoted labels and explanations:
- • "AI Followers":"They wait for proven ROI and best practices before moving. They let others take the risks. They prioritize stability and compliance over speed and possibility," said Solis. - • "AI Forward":"Integrating AI alongside humans deliberately...the human is always in the loop by design," Solis said. - • "AI First": Solis described this stage as where "AI should be the default solution for an increasing number of tasks, with human intervention as the exception rather than the rule." - • "AI Native": Solis said a product in this stage "wouldn't exist if AI wasn't part of the equation."
Editorial analysis - technical context
Industry practitioners commonly use staged adoption frameworks to map capability and risk. Comparable taxonomies help teams prioritize tooling, data pipelines, and human-in-the-loop controls at each maturity level. For engineering teams, moving from pilot projects to production at scale typically raises data quality, monitoring, and retraining challenges.
Industry context
Observers and consultants often frame AI adoption as a spectrum from cautious experimentation to product-embedded AI. Reporting that uses labeled stages, like Solis's, aids cross-functional conversations but does not specify implementation choices such as model architecture, inference strategies, or governance mechanisms.
What to watch
Indicators that map to these stages include standardized ROI measurements, human-in-loop design patterns, automation rates for routine tasks, and new product features that are infeasible without AI. Publications that define stage names help teams align language but do not replace technical roadmaps.
Reported sources
All quoted material and the staged framework appear in the Vicki Salemi column for The Tribune, republished on Brian Solis's site on June 21, 2026.
Scoring Rationale #
This is a concept and leadership framing piece rather than new technology or benchmark. It helps practitioners with adoption language and alignment but does not introduce technical methods or measurements.
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